The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article: Bone Joint Res 2023;12(7):447–454

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article: Bone Joint J 2022;104-B(12):1292–1303

Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article: Bone Joint J 2022;104-B(8):911–914

Aims. Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. Methods. The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. Results. Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. Conclusion. These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929–937

Aims. The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons. Methods. Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes. Results. The four intersecting themes identified were: 1) validity in traditional research, 2) confusion around the definition of AI, 3) an inability to validate AI research, and 4) cautious optimism about AI research. Underpinning these themes is the notion of a validity heuristic that is strongly rooted in traditional research teaching and embedded in medical and surgical training. Conclusion. Research involving AI sometimes challenges the accepted traditional evidence-based framework. This can give rise to confusion among orthopaedic surgeons, who may be unable to confidently validate findings. In our study, the impact of this was mediated by cautious optimism based on an ingrained validity heuristic that orthopaedic surgeons develop through their medical training. Adding to this, the integration of AI into everyday life works to reduce suspicion and aid acceptance. Cite this article: Bone Jt Open 2023;4(9):696–703

Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality

This annotation briefly reviews the history of artificial intelligence and machine learning in health care and orthopaedics, and considers the role it will have in the future, particularly with reference to statistical analyses involving large datasets. Cite this article: Bone Joint J 2019;101-B:1476–1478

Aims. This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. Methods. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate. Results. Time series clustering allowed us to divide the patients into two groups, and the predictive factors were identified including patient- and operation-related factors. The area under the receiver operating characteristic (ROC) curve (AUC) for the BMD loss prediction averaged 0.734. Virtual administration of bisphosphonate showed on average 14% efficacy in preventing BMD loss of zone 7. Additionally, stem types and preoperative triglyceride (TG), creatinine (Cr), estimated glomerular filtration rate (eGFR), and creatine kinase (CK) showed significant association with the estimated patient-specific efficacy of bisphosphonate. Conclusion. Periprosthetic BMD loss after THA is predictable based on patient- and operation-related factors, and optimal prescription of bisphosphonate based on the prediction may prevent BMD loss. Cite this article: Bone Joint Res 2024;13(4):184–192

Cite this article: Bone Joint Res 2023;12(8):494–496.

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks.

Cite this article: Bone Joint J 2021;103-B(9):1442–1448.

In the last decades, the use of artificial intelligence (AI) has been increasingly investigated in intervertebral disc degeneration (IDD) and chronic low back pain (LBP) research. To date, several AI-based cutting-edge technologies, such as computer vision, computer-assisted diagnosis, decision support system and natural language processing have been utilized to optimize LBP prevention, diagnosis, and treatment. This talk will provide an outline on contemporary AI applications to IDD and LBP research, with a particular attention towards actual knowledge gaps and promising innovative tools

Over 8000 total hip arthroplasties (THA) in the UK were revised in 2019, half for aseptic loosening. It is believed that Artificial Intelligence (AI) could identify or predict failing THA and result in early recognition of poorly performing implants and reduce patient suffering. The aim of this study is to investigate whether Artificial Intelligence based machine learning (ML) / Deep Learning (DL) techniques can train an algorithm to identify and/or predict failing uncemented THA. Consent was sought from patients followed up in a single design, uncemented THA implant surveillance study (2010–2021). Oxford hip scores and radiographs were collected at yearly intervals. Radiographs were analysed by 3 observers for presence of markers of implant loosening/failure: periprosthetic lucency, cortical hypertrophy, and pedestal formation. DL using the RGB ResNet 18 model, with images entered chronologically, was trained according to revision status and radiographic features. Data augmentation and cross validation were used to increase the available training data, reduce bias, and improve verification of results. 184 patients consented to inclusion. 6 (3.2%) patients were revised for aseptic loosening. 2097 radiographs were analysed: 21 (11.4%) patients had three radiographic features of failure. 166 patients were used for ML algorithm testing of 3 scenarios to detect those who were revised. 1) The use of revision as an end point was associated with increased variability in accuracy. The area under the curve (AUC) was 23–97%. 2) Using 2/3 radiographic features associated with failure was associated with improved results, AUC: 75–100%. 3) Using 3/3 radiographic features, had less variability, reduced AUC of 73%, but 5/6 patients who had been revised were identified (total 66 identified). The best algorithm identified the greatest number of revised hips (5/6), predicting failure 2–8 years before revision, before all radiographic features were visible and before a significant fall in the Oxford Hip score. True-Positive: 0.77, False Positive: 0.29. ML algorithms can identify failing THA before visible features on radiographs or before PROM scores deteriorate. This is an important finding that could identify failing THA early

Bone age is a radiographical assessment used in pediatric medicine due to its relative objectivity in determining biological maturity compared to chronological age and size.1 Currently, Greulich and Pyle (GP) is one of the most common methods used to determine bone age from hand radiographs.2–4 In recent years, new methods were developed to increase the efficiency in bone age analysis like the shorthand bone age (SBA) and the automated artificial intelligence algorithms. The purpose of this study is to evaluate the accuracy and reliability of these two methods and examine if the reduction in analysis time compromises their accuracy. Two hundred thirteen males and 213 females were selected. Each participant had their bone age determined by two separate raters using the GP (M1) and SBA methods (M2). Three weeks later, the two raters repeated the analysis of the radiographs. The raters timed themselves using an online stopwatch while analyzing the radiograph on a computer screen. De-identified radiographs were securely uploaded to an automated algorithm developed by a group of radiologists in Toronto. The gold standard was determined to be the radiology report attached to each radiograph, written by experienced radiologists using GP (M1). For intra-rater variability, intraclass correlation analysis between trial 1 (T1) and trial 2 (T2) for each rater and method was performed. For inter-rater variability, intraclass correlation was performed between rater 1 (R1) and rater 2 (R2) for each method and trial. Intraclass correlation between each method and the gold standard fell within the 0.8–0.9 range, highlighting significant agreement. Most of the comparisons showed a statistically significant difference between the two new methods and the gold standard; however it may not be clinically significant as it ranges between 0.25–0.5 years. A bone age is considered clinically abnormal if it falls outside 2 standard deviations of the chronological age; standard deviations are calculated and provided in GP atlas.6–8 For a 10-year old female, 2 standard deviations constitute 21.6 months which far outweighs the difference reported here between SBA, automated algorithm and the gold standard. The median time for completion using the GP method was 21.83 seconds for rater 1 and 9.30 seconds for rater 2. In comparison, SBA required a median time of 7 seconds for rater 1 and 5 seconds for rater 2. The automated method had no time restraint as bone age was determined immediately upon radiograph upload. The correlation between the two trials in each method and rater (i.e. R1M1T1 vs R1M1T2) was excellent (κ= 0.9–1) confirming the reliability of the two new methods. Similarly, the correlation between the two raters in each method and trial (i.e. R1M1T1 vs R2M1T1) fell within the 0.9–1 range. This indicates a limited variability between raters who may use these two methods. The shorthand bone age method and an artificial intelligence automated algorithm produced values that are in agreement with the gold standard Greulich and Pyle, while reducing analysis time and maintaining a high inter-rater and intra-rater reliability

As patient data continues to grow, the importance of efficient and precise analysis cannot be overstated. The employment of Generative Artificial Intelligence (AI), specifically Chat GPT-4, in the realm of medical data interpretation has been on the rise. However, its effectiveness in comparison to manual data analysis has been insufficiently investigated. This quality improvement project aimed to evaluate the accuracy and time-efficiency of Generative AI (GPT-4) against manual data interpretation within extensive datasets pertaining to patients with orthopaedic injuries. A dataset, containing details of 6,562 orthopaedic trauma patients admitted to a district general hospital over a span of two years, was reviewed. Two researchers operated independently: one utilised GPT-4 for insights via prompts, while the other manually examined the identical dataset employing Microsoft Excel and IBM® SPSS® software. Both were blinded on each other's procedures and outcomes. Each researcher answered 20 questions based on the dataset including injury details, age groups, injury specifics, activity trends and the duration taken to assess the data. Upon comparison, both GPT-4 and the manual researcher achieved consistent results for 19 out of the 20 questions (95% accuracy). After a subsequent review and refined prompts (prompt engineering) to GPT-4, the answer to the final question aligned with the manual researcher's findings. GPT-4 required just 30 minutes, a stark contrast to the manual researcher's 9-hour analytical duration. This quality improvement project emphasises the transformative potential of Generative AI in the domain of medical data analysis. GPT-4 not only paralleled the accuracy of manual analysis but also achieved this in significantly less time. For optimal accurate results, data analysis by AI can be enhanced through human oversight. Adopting AI-driven approaches, particularly in orthopaedic data interpretation, can enhance efficiency and ultimately improve patient care. We recommend future investigations on large and more varied datasets to reaffirm these outcomes

INTRODUCTION. Quality monitoring is increasingly important to support and assure sustainability of the Orthopaedic practice. Many surgeons in a non-academic setting lack the resources to accurately monitor quality of care. Widespread use of electronic medical records (EMR) provides easier access to medical information and facilitates its analysis. However, manual review of EMRs is inefficient and costly. Artificial Intelligence (AI) software has allowed for development of automated search algorithms for extracting relevant complications from EMRs. We questioned whether an AI supported algorithm could be used to provide accurate feedback on the quality of care following Total Hip Arthroplasty (THA) in a high-volume, non-academic setting. METHODS. 532 Consecutive patients underwent 613 THA between January 1. st. and December 31. st. , 2017. Patients were prospectively followed pre-op, 6 weeks, 3 months and 1 year. They were seen by the surgeon who created clinical notes and reported every adverse event. A random derivation cohort (100 patients, 115 hips) was used to determine accuracy. The algorithm was compared to manual extraction to validate performance in raw data extraction. The full cohort (532 patients, 613 hips) was used to determine its recall, precision and F-value. RESULTS. The algorithm had an accuracy value of 95.0%, compared to 94.5% for manual review (p=0.69). Recall of 96.0% was achieved with precision of 88.0% and F-measure of 0.85 for all adverse events. Recovery of 80.6% of patients was completely uneventful. Re-intervention was required in 1.3% of cases and 18.1% had a ‘transient’ event such as low back pain. The infection and dislocation rate was 0,3%. CONCLUSION. An AI supported search algorithm can analyze and interpret large quantities of EMRs at greater speed but with performance comparable to manual review. Using the program, new clinical information surfaced. 18.1% of patients can be expected to have a ‘transient’ problem following a THA procedure

Introduction and Objective

Up to 30% of thoracolumbar (TL) fractures are missed in the emergency room. Failure to identify these fractures can result in neurological injuries up to 51% of the casesthis article aimed to clarify the incidence and risk factors of traumatic fractures in China. The China National Fracture Study (CNFS. Obtaining sagittal and anteroposterior radiographs of the TL spine are the first diagnostic step when suspecting a traumatic injury. In most cases, CT and/or MRI are needed to confirm the diagnosis. These are time and resource consuming. Thus, reliably detecting vertebral fractures in simple radiographic projections would have a significant impact. We aim to develop and validate a deep learning tool capable of detecting TL fractures on lateral radiographs of the spine. The clinical implementation of this tool is anticipated to reduce the rate of missed vertebral fractures in emergency rooms.

Polyethylene wear represents a significant risk factor for the long-term success of knee arthroplasty [1]. This work aimed to develop and in vivo validate an automated algorithm for accurate and precise AI based wear measurement in knee arthroplasty using clinical AP radiographs for scientifically meaningful multi-centre studies.

Twenty postoperative radiographs (knee joint AP in standing position) after knee arthroplasty were analysed using the novel algorithm. A convolutional neural network-based segmentation is used to localize the implant components on the X-Ray, and a 2D-3D registration of the CAD implant models precisely calculates the three-dimensional position and orientation of the implants in the joint at the time of acquisition. From this, the minimal distance between the involved implant components is determined, and its postoperative change over time enables the determination of wear in the radiographs.

The measured minimum inlay height of 335 unloaded inlays excluding the weight-induced deformation, served as ground truth for validation and was compared to the algorithmically calculated component distances from 20 radiographs.

With an average weight of 94 kg in the studied TKA patient cohort, it was determined that an average inlay height of 6.160 mm is expected in the patient. Based on the radiographs, the algorithm calculated a minimum component distance of 6.158 mm (SD = 81 µm), which deviated by 2 µm in comparison to the expected inlay height.

An automated method was presented that allows accurate and precise determination of the inlay height and subsequently the wear in knee arthroplasty based on a clinical radiograph and the CAD models. Precision and accuracy are comparable to the current gold standard RSA [2], but without relying on special radiographic setups. The developed method can therefore be used to objectively investigate novel implant materials with meaningful clinical cohorts, thus improving the quality of patient care.

Arthroplasties are widely performed to improve mobility and quality of life for symptomatic knee/hip osteoarthritis patients. With increasing rates of Total Joint Replacements in the United Kingdom, predicting length of stay is vital for hospitals to control costs, manage resources, and prevent postoperative complications. A longer Length of stay has been shown to negatively affect the quality of care, outcomes and patient satisfaction. Thus, predicting LOS enables us to make full use of medical resources.

Clinical characteristics were retrospectively collected from 1,303 patients who received TKA and THR. A total of 21 variables were included, to develop predictive models for LOS by multiple machine learning (ML) algorithms, including Random Forest Classifier (RFC), K-Nearest Neighbour (KNN), Extreme Gradient Boost (XgBoost), and Na¯ve Bayes (NB). These models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance. A feature selection approach was used to identify optimal predictive factors. Based on the ROC of Training result, XgBoost algorithm was selected to be applied to the Test set.

The areas under the ROC curve (AUCs) of the 4 models ranged from 0.730 to 0.966, where higher AUC values generally indicate better predictive performance. All the ML-based models performed better than conventional statistical methods in ROC curves. The XgBoost algorithm with 21 variables was identified as the best predictive model. The feature selection indicated the top six predictors: Age, Operation Duration, Primary Procedure, BMI, creatinine and Month of Surgery.

By analysing clinical characteristics, it is feasible to develop ML-based models for the preoperative prediction of LOS for patients who received TKA and THR, and the XgBoost algorithm performed the best, in terms of accuracy of predictive performance. As this model was originally crafted at Ashford and St. Peters Hospital, we have naturally named it as THE ASHFORD OUTCOME.

Background

Dislocation is a common complication following total hip arthroplasty (THA), and accounts for a high percentage of subsequent revisions. The purpose of this study was to develop a convolutional neural network (CNN) model to identify patients at high risk for dislocation based on postoperative anteroposterior (AP) pelvis radiographs.

The pathogenesis of scoliosis progression remains poorly understood. Seventy-two subject data sets, consisting of four successive values of Cobb-angle and lateral deviations at apices for six and twelve-months intervals in the coronal plane, were used to train and test an artificial neural network (ANN) to predict spinal deformity progression. The accuracies of the trained ANN (3-4-1) for training and testing data were within 3.64° (±2.58°) and 4.40° (±1.86°) of Cobb angles, and within 3.59 (±3.96) mm and 3.98 (±3.41) mm of lateral deviations, respectively. The adapted technique for predicting the scoliosis deformity progression has promising clinical applications.

Scoliosis is a common and poorly understood three-dimensional spinal deformity. The study purpose is to predict scoliosis progression at six and twelve months intervals in the future using successive spinal indices with an artificial neural network (ANN).

The adapted ANN technique enables earlier detection of scoliosis progression with high accuracy. Improved prediction of scoliosis progression will impact bracing or surgical treatment decisions, and may decrease hazardous X-ray exposure.

Seventy-two data sets from adolescent idiopathic scoliosis subjects recruited at the Alberta Children’s Hospital were used in this study. Data sets composed of four successive values of Cobb angles and lateral deviations at apices for six and twelvemonth intervals (coronal plane) were extracted to train and test a specific ANN for predicting scoliosis progression.

Progression patterns in Cobb angles (n = 10) and lateral deviations (n = 8) were successfully identified. The accuracies of the trained ANN (3-4-1) with the training and testing data sets were 3.64° (±2.58°) and 4.40° (±1.86°) of Cobb angles, 3.59 (±3.96) mm and 3.98 (±3.41) mm of lateral deviations, respectively. These results are in close agreement with those using cubic spline extrapolation techniques (3.49° ± 1.85° and 3.31 ± 4.22 mm) and adaptive neuro-fuzzy inference system (3.92° ±3.53° and 3.37 ±3.95 mm) for the same testing data.

ANN can be a promising technique for prediction of scoliosis progression with substantial improvements in accuracy over current techniques, leading to potentially important implications for scoliosis monitoring and treatment decisions.

Funding: AHFMR, CIHR, Fraternal Order of Eagles, NSERC, GEOIDE.

Aims. To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Methods. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation. Results. For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts. Conclusion. The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts. Cite this article: Bone Joint J 2024;106-B(7):688–695

Aims. Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article: Bone Joint J 2021;103-B(12):1754–1758

The OpenAI chatbot ChatGPT is an artificial intelligence (AI) application that uses state-of-the-art language processing AI. It can perform a vast number of tasks, from writing poetry and explaining complex quantum mechanics, to translating language and writing research articles with a human-like understanding and legitimacy. Since its initial release to the public in November 2022, ChatGPT has garnered considerable attention due to its ability to mimic the patterns of human language, and it has attracted billion-dollar investments from Microsoft and PricewaterhouseCoopers. The scope of ChatGPT and other large language models appears infinite, but there are several important limitations. This editorial provides an introduction to the basic functionality of ChatGPT and other large language models, their current applications and limitations, and the associated implications for clinical practice and research. Cite this article: Bone Joint J 2023;105-B(6):587–589

Aims. Hip dysplasia (HD) leads to premature osteoarthritis. Timely detection and correction of HD has been shown to improve pain, functional status, and hip longevity. Several time-consuming radiological measurements are currently used to confirm HD. An artificial intelligence (AI) software named HIPPO automatically locates anatomical landmarks on anteroposterior pelvis radiographs and performs the needed measurements. The primary aim of this study was to assess the reliability of this tool as compared to multi-reader evaluation in clinically proven cases of adult HD. The secondary aims were to assess the time savings achieved and evaluate inter-reader assessment. Methods. A consecutive preoperative sample of 130 HD patients (256 hips) was used. This cohort included 82.3% females (n = 107) and 17.7% males (n = 23) with median patient age of 28.6 years (interquartile range (IQR) 22.5 to 37.2). Three trained readers’ measurements were compared to AI outputs of lateral centre-edge angle (LCEA), caput-collum-diaphyseal (CCD) angle, pelvic obliquity, Tönnis angle, Sharp’s angle, and femoral head coverage. Intraclass correlation coefficients (ICC) and Bland-Altman analyses were obtained. Results. Among 256 hips with AI outputs, all six hip AI measurements were successfully obtained. The AI-reader correlations were generally good (ICC 0.60 to 0.74) to excellent (ICC > 0.75). There was lower agreement for CCD angle measurement. Most widely used measurements for HD diagnosis (LCEA and Tönnis angle) demonstrated good to excellent inter-method reliability (ICC 0.71 to 0.86 and 0.82 to 0.90, respectively). The median reading time for the three readers and AI was 212 (IQR 197 to 230), 131 (IQR 126 to 147), 734 (IQR 690 to 786), and 41 (IQR 38 to 44) seconds, respectively. Conclusion. This study showed that AI-based software demonstrated reliable radiological assessment of patients with HD with significant interpretation-related time savings. Cite this article: Bone Jt Open 2022;3(11):877–884

Aims. Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli. Results. A total of 932 bilateral full-limb radiographs (1,864 knees) were measured at a rate of 20.63 seconds/image. The knee alignment using the radiological ankle centre was accurate against ground truth radiologist measurements (inter-class correlation coefficient (ICC) = 0.99 (0.98 to 0.99)). Compared to the radiological ankle centre, the mean midpoint of the malleoli was 2.3 mm (SD 1.3) lateral and 5.2 mm (SD 2.4) distal, shifting alignment by 0.34. o. (SD 2.4. o. ) valgus, whereas the midpoint of the soft-tissue sulcus was 4.69 mm (SD 3.55) lateral and 32.4 mm (SD 12.4) proximal, shifting alignment by 0.65. o. (SD 0.55. o. ) valgus. On the intermalleolar line, measuring a point at 46% (SD 2%) of the intermalleolar width from the medial malleoli (2.38 mm medial adjustment from midpoint) resulted in knee alignment identical to using the radiological ankle centre. Conclusion. The current study leveraged AI to create a consistent and objective model that can estimate patient-specific adjustments necessary for optimal landmark usage in extramedullary and computer-guided navigation for tibial coronal alignment to match radiological planning. Cite this article: Bone Jt Open 2022;3(10):767–776

Aims. The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. Methods. In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. Results. In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). Conclusion. The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574–1581

Aims

The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs?

The variables involved in a robotic THA can exceed 52: many parameters as pelvic orientation with CT scan, templating, offset, and leg-length, acetabular reaming, femoral osteotomy, mapping the anatomy; predefining safe zones, robotic execution, femoral head size, thickness of PE etc. with several variables for each parameter, with a total number of variables exceeding 52. This familiar number is the number of cards in a standard deck. The number of possible combinations (factorial 52! = 10^67) to shuffle the cards (and may be to perform a THA) is greater than the number of atoms on earth! Thinking that artificial intelligence and robotics can solve these problems, some surgeons and implant manufacturers have turned to artificial intelligence and robotics. We asked two questions:1) can robot with artificial intelligence really process 52 variables that represent 10^67 combinations? 2) the safety of the technology was ascertained by interrogating Food and Drug Administration (FDA) database about software-related recalls in computer-assisted and robotic arthroplasty [1], between 2017 and 2022. 1). The best computers can only calculate around 100 thousand billion combinations (10^14), and with difficulty: it takes more than 100 days to arrive at this number of digits (10^14) after the decimal point for the number π (pi). We can, therefore, expect the robot to be imperfect. 2). For the FDA software-related recalls, 4634 units were involved. The FDA determined root causes were: software design (66.6%), design change (22.2%), manufacturing deployment (5.6%), design manufacturing process (5.6%). Among the manufacturers’ reasons for recalls, a specific error was declared in 88.9%. a coding error in 43.8%. 94.4% software-related recalls were classified as class 2. Return of the device was the main action taken by firms (44.4%), followed by software update (38.9%). 3). In the same period, no robot complained about its surgeon!. Hip surgeon is as intelligent as a robot and almost twice as safe

The August 2023 Research Roundup. 360. looks at: Can artificial intelligence improve the readability of patient education materials?; What is the value of radiology input during a multidisciplinary orthopaedic oncology conference?; Periprosthetic joint infection in patients with multiple arthroplasties; Orthopedic Surgery and Anesthesiology Surgical Improvement Strategies Project - Phase III outcomes; Knot tying in arthroplasty and arthroscopy causes lesions to surgical gloves: a potential risk of infection; Vascular calcification of the ankle in plain radiographs equals diabetes mellitus?

The August 2023 Knee Roundup. 360. looks at: Curettage and cementation of giant cell tumour of bone: is arthritis a given?; Anterior knee pain following total knee arthroplasty: does the patellar cement-bone interface affect postoperative anterior knee pain?; Nickel allergy and total knee arthroplasty; The use of artificial intelligence for the prediction of periprosthetic joint infection following aseptic revision total knee arthroplasty; Ambulatory unicompartmental knee arthroplasty: development of a patient selection tool using machine learning; Femoral asymmetry: a missing piece in knee alignment; Needle arthroscopy – a benefit to patients in the outpatient setting; Can lateral unicompartmental knees be done in a day-case setting?

The June 2024 Wrist & Hand Roundup. 360. looks at: One-year outcomes of the anatomical front and back reconstruction for scapholunate dissociation; Limited intercarpal fusion versus proximal row carpectomy in the treatment of SLAC or SNAC wrist: results after 3.5 years; Prognostic factors for clinical outcomes after arthroscopic treatment of traumatic central tears of the triangular fibrocartilage complex; The rate of nonunion in the MRI-detected occult scaphoid fracture: a multicentre cohort study; Does correction of carpal malalignment influence the union rate of scaphoid nonunion surgery?; Provision of a home-based video-assisted therapy programme in thumb carpometacarpal arthroplasty; Is replantation associated with better hand function after traumatic hand amputation than after revision amputation?; Diagnostic performance of artificial intelligence for detection of scaphoid and distal radius fractures: a systematic review

The October 2023 Wrist & Hand Roundup. 360. looks at: Distal radius fracture management: surgeon factors markedly influence decision-making; Fracture-dislocation of the radiocarpal joint: bony and capsuloligamentar management, outcomes, and long-term complications; Exploring the role of artificial intelligence chatbot in the management of scaphoid fractures; Role of ultrasonography for evaluation of nerve recovery in repaired median nerve lacerations; Four weeks versus six weeks of immobilization in a cast following closed reduction for displaced distal radial fractures in adult patients: a multicentre randomized controlled trial; Rehabilitation following flexor tendon injury in Zone 2: a randomized controlled study; On the road again: return to driving following minor hand surgery; Open versus single- or dual-portal endoscopic carpal tunnel release: a meta-analysis of randomized controlled trials

Aims

To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials.

Aims

This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images.

Aims. While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability. Methods. We posed the 20 most Google-searched questions about THA and TKA, plus ten additional postoperative questions, to ChatGPT. Each question was asked twice to evaluate for consistency in quality. Following each response, we responded with, “Please explain so it is easier to understand,” to evaluate ChatGPT’s ability to reduce response reading grade level, measured as Flesch-Kincaid Grade Level (FKGL). Five resident physicians rated the 120 responses on 1 to 5 accuracy and comprehensiveness scales. Additionally, they answered a “yes” or “no” question regarding acceptability. Mean scores were calculated for each question, and responses were deemed acceptable if ≥ four raters answered “yes.”. Results. The mean accuracy and comprehensiveness scores were 4.26 (95% confidence interval (CI) 4.19 to 4.33) and 3.79 (95% CI 3.69 to 3.89), respectively. Out of all the responses, 59.2% (71/120; 95% CI 50.0% to 67.7%) were acceptable. ChatGPT was consistent when asked the same question twice, giving no significant difference in accuracy (t = 0.821; p = 0.415), comprehensiveness (t = 1.387; p = 0.171), acceptability (χ. 2. = 1.832; p = 0.176), and FKGL (t = 0.264; p = 0.793). There was a significantly lower FKGL (t = 2.204; p = 0.029) for easier responses (11.14; 95% CI 10.57 to 11.71) than original responses (12.15; 95% CI 11.45 to 12.85). Conclusion. ChatGPT answered THA and TKA patient questions with accuracy comparable to previous reports of websites, with adequate comprehensiveness, but with limited acceptability as the sole information source. ChatGPT has potential for answering patient questions about THA and TKA, but needs improvement. Cite this article: Bone Jt Open 2024;5(2):139–146

Aims. Hip disease is common in children with cerebral palsy (CP) and can decrease quality of life and function. Surveillance programmes exist to improve outcomes by treating hip disease at an early stage using radiological surveillance. However, studies and surveillance programmes report different radiological outcomes, making it difficult to compare. We aimed to identify the most important radiological measurements and develop a core measurement set (CMS) for clinical practice, research, and surveillance programmes. Methods. A systematic review identified a list of measurements previously used in studies reporting radiological hip outcomes in children with CP. These measurements informed a two-round Delphi study, conducted among orthopaedic surgeons and specialist physiotherapists. Participants rated each measurement on a nine-point Likert scale (‘not important’ to ‘critically important’). A consensus meeting was held to finalize the CMS. Results. Overall, 14 distinct measurements were identified in the systematic review, with Reimer’s migration percentage being the most frequently reported. These measurements were presented over the two rounds of the Delphi process, along with two additional measurements that were suggested by participants. Ultimately, two measurements, Reimer’s migration percentage and femoral head-shaft angle, were included in the CMS. Conclusion. This use of a minimum standardized set of measurements has the potential to encourage uniformity across hip surveillance programmes, and may streamline the development of tools, such as artificial intelligence systems to automate the analysis in surveillance programmes. This core set should be the minimum requirement in clinical studies, allowing clinicians to add to this as needed, which will facilitate comparisons to be drawn between studies and future meta-analyses. Cite this article: Bone Jt Open 2023;4(11):825–831

Abstract. Introduction. Knee braces are limited to providing passive support. There is currently no brace available providing both continuous monitoring and active robot-assisted movements of the knee joint. This project aimed to develop a wearable intelligent motorised robotic knee brace to support and monitor rehabilitation for a range of knee conditions including post-surgical rehabilitation. This brace can be used at home providing ambulatory continuous passive movement obviating the need for hospital admissions. Methodology. A wearable sensing system monitoring knee range of motion was developed to provide remote feedback to clinicians and real-time guidance for patients. A prototype of an exoskeleton providing dynamic motion assistance was developed to help patients complete their exercise goals and strengthen their muscles. The accuracy and reliability of those functions were validated in human participants during exercises including knee flexion/extension (FE) in bed and in chair, sit-to-stand and stand-to-sit. Results. The knee FE measurement from the sensing system showed high accuracy (correlation coefficient of 0.99°) in human participants. The real-time FE data during exercises showed that the desired exoskeleton rotation fitted well with the participant's knee rotation. This indicated the exoskeleton could coordinate with the participant's knee motion by providing consistent motion assistance. The development of user interfaces to provide feedback is currently underway. Conclusion. A wearable robotic knee brace to monitor and support knee rehabilitation exercises was successfully developed. Further development of this device with the use of artificial intelligence has the potential to aid patient rehabilitation in a variety of knee conditions

Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train machine learning (ML) algorithms, based on answers from digitalized history taking questionnaires, to predict treatment of hip osteoartritis (either conservative or surgical); (2) such an algorithm could streamline clinical consultation. Multiple ML models were trained on 600 annotated (80% training, 20% test) digital history taking questionnaires, acquired before consultation. Best performing models, based on balanced accuracy and optimized automated hyperparameter tuning, were build into our daily clinical orthopedic practice. Fifty patients with hip complaints (>45 years) were prospectively predicted and planned (partly blinded, partly unblinded) for consultation with the physician assistant (conservative) or orthopedic surgeon (operative). Tailored patient information based on the prediction was automatically sent to a smartphone app. Level of evidence: IV. Random Forest and BernoulliNB were the most accurate ML models (0.75 balanced accuracy). Treatment prediction was correct in 45 out of 50 consultations (90%), p<0.0001 (sign and binomial test). Specialized consultations where conservatively predicted patients were seen by the physician assistant and surgical patients by the orthopedic surgeon were highly appreciated and effective. Treatment strategy of hip osteoartritis based on answers from digital history taking questionnaires was accurately predicted before patients entered the hospital. This can make outpatient consultation scheduling more efficient and tailor pre-consultation patient education

Gait analysis is an indispensable tool for scientific assessment and treatment of individuals whose ability to walk is impaired. The high cost of installation and operation are a major limitation for wide-spread use in clinical routine. Advances in Artificial Intelligence (AI) could significantly reduce the required instrumentation. A mobile phone could be all equipment necessary for 3D gait analysis. MediaPipe Pose provided by Google Research is such a Machine Learning approach for human body tracking from monocular RGB video frames that is detecting 3D-landmarks of the human body. Aim of this study was to analyze the accuracy of gait phase detection based on the joint landmarks identified by the AI system. Motion data from 10 healthy volunteers walking on a treadmill with a fixed speed of 4.5km/h (Callis, Sprintex, Germany) was sampled with a mobile phone (iPhone SE 2nd Generation, Apple). The video was processed with Mediapipe Pose (Version 0.9.1.0) using custom python software. Gait phases (Initial Contact - IC and Toe Off - TO) were detected from the angular velocities of the lower legs. For the determination of ground truth, the movement was simultaneously recorded with the AS-200 System (LaiTronic GmbH, Innsbruck, Austria). The number of detected strides, the error in IC detection and stance phase duration was calculated. In total, 1692 strides were detected from the reference system during the trials from which the AI-system identified 679 strides. The absolute mean error (AME) in IC detection was 39.3 ± 36.6 ms while the AME for stance duration was 187.6 ± 140 ms. Landmark detection is a challenging task for the AI-system as can clearly be seen be the rate of only 40% detected strides. As mentioned by Fadillioglu et al., error in TO-detection is higher than in IC-detection

Osteoarthritis (OA) is a degenerative joint disease affecting millions worldwide. Early detection of OA and monitoring its progression is essential for effective treatment and for preventing irreversible damage. Although sensors have emerged as a promising tool for monitoring analytes in patients, their application for monitoring the state of pathology is currently restricted to specific fields (such as diabetes). In this study, we present the development of an optical sensor system for real-time monitoring of inflammation based on the measurement of nitric oxide (NO), a molecule highly produced in tissues during inflammation. Single-walled carbon nanotubes (SWCNT) were functionalized with a single-stranded DNA (ssDNA) wrapping designed using an artificial intelligence approach and tested using S-nitroso-N-acetyl penicillamine (SNAP) as a standard released-NO marker. An optical SWIR reader with LED excitation at 650 nm, 730 nm and detecting emission above 1000 nm was developed to read the fluorescence signal from the SWCNTs. Finally, the SWCNT was embedded in GelMa to prove the feasibility of monitoring the release of NO in bovine chondrocyte and osteochondral inflamed cultures (1–10 ng/ml IL1β) monitored over 48 hours. The stability of the inflammation model and NO release was indirectly validated using the Griess and DAF-FM methods. A microfabricated sensor tag was developed to explore the possibility of using ssDNA-SWCNT in an ex vivo anatomic set-up for surgical feasibility, the limit of detection, and the stability under dynamic flexion. The SWCNT sensor was sensitive to NO in both in silico and in vitro conditions during the inflammatory response from chondrocyte and osteochondral plug cultures. The fluorescence signal decreased in the inflamed group compared to control, indicating increased NO concentration. The micro-tag was suitable and stable in joints showing a readable signal at a depth of up to 6 mm under the skin. The ssDNA-SWCNT technology showed the possibility of monitoring inflammation continuously in an in vitro set-up and good stability inside the joint. However, further studies in vivo are needed to prove the possibility of monitoring disease progression and treatment efficacy in vivo. Acknowledgments: The project was co-financed by Innosuisse (grant nr. 56034.1 IP-LS)

The objectives of this study are to evaluate the impact of the CoVID-19 pandemic on the development of relevant emerging digital healthcare trends and to explore which digital healthcare trend does the health industry need most to support HCPs. A web survey using 39 questions facilitating Five-Point Likert scales was performed from 1.8.2020 – 31.10.2020. Of 260 participants invited, 90 participants answered the questionnaire. The participants were located in the Hospital/HCP sector in 11.9%, in other healthcare sectors in 22.2%, in the pharmaceutical sector in 11.1%, in the medical device and equipment industry in 43.3%. The Five-Point Likert scales were in all cases fashioned as from 1 (strongly disagree) to 5 (strongly agree). As the top 3 most impacted digital health care trends strongly impacted by CoVID-19, respondents named:. - remote management of patients by telemedicine, mean answer 4.44. - shared data governance under patient control, mean answer 3.80. - new virtual interaction between HCP´s and medical industry, mean answer 3.76. Respondents were asked which level of readiness of the healthcare system currently possess to cope with the current trend impacted by CoVID-19. - Digital and efficient healthcare logistics, mean answer 1.54. - Integrated health care, mean answer 1.73. - Use of big data and artificial intelligence, mean answer 2.03. Asked if collaborative research in the form of digital data platforms for research data sharing and increasing collaboration with multi-centric consortia would have a positive impact on the healthcare sector, the agreement was high with a value of mean 4.10 on the scale. We can conclude that the impact of COVID-19 appears to be a high agreement of necessary advances in digitalization in the health care sector and in the collaboration of HCPs with the health care industry. Health care professional are unsure, in how far the national health care sector is capable of transformation in healthcare logistics and integrated health care

Single level discectomy (SLD) is one of the most commonly performed spinal surgery procedures. Two key drivers of their cost-of-care are duration of surgery (DOS) and postoperative length of stay (LOS). Therefore, the ability to preoperatively predict SLD DOS and LOS has substantial implications for both hospital and healthcare system finances, scheduling and resource allocation. As such, the goal of this study was to predict DOS and LOS for SLD using machine learning models (MLMs) constructed on preoperative factors using a large North American database. The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for SLD procedures from 2014-2019. The dataset was split in a 60/20/20 ratio of training/validation/testing based on year. Various MLMs (traditional regression models, tree-based models, and multilayer perceptron neural networks) were used and evaluated according to 1) mean squared error (MSE), 2) buffer accuracy (the number of times the predicted target was within a predesignated buffer), and 3) classification accuracy (the number of times the correct class was predicted by the models). To ensure real world applicability, the results of the models were compared to a mean regressor model. A total of 11,525 patients were included in this study. During validation, the neural network model (NNM) had the best MSEs for DOS (0.99) and LOS (0.67). During testing, the NNM had the best MSEs for DOS (0.89) and LOS (0.65). The NNM yielded the best 30-minute buffer accuracy for DOS (70.9%) and ≤120 min, >120 min classification accuracy (86.8%). The NNM had the best 1-day buffer accuracy for LOS (84.5%) and ≤2 days, >2 days classification accuracy (94.6%). All models were more accurate than the mean regressors for both DOS and LOS predictions. We successfully demonstrated that MLMs can be used to accurately predict the DOS and LOS of SLD based on preoperative factors. This big-data application has significant practical implications with respect to surgical scheduling and inpatient bedflow, as well as major implications for both private and publicly funded healthcare systems. Incorporating this artificial intelligence technique in real-time hospital operations would be enhanced by including institution-specific operational factors such as surgical team and operating room workflow

Precision medicine tailoring the patient pathway based on the risk, prognosis, and treatment response may bring benefits to the patients. To identify risk factors contributing to the early failure of treatment (development of events of interest) and when possible to change the prognosis via modifying these factors may improve the outcome and/or lower the risk of complications. There is an emerging goal to identify such parameters in total knee arthroplasty (TKA) thus lower the risk of revision surgery. The goal of this study was to identify factors explaining the risk for early revision of TKA using an artificial intelligence method appropriate for this task. We applied a patient similarity network (PSN) for the identification of risk factors associated with early reoperations (n=109, 5.8%) in patients with TKA (n=1885). Next, an algorithm based on formal concept analysis was developed to support the patient decision on how to change modifying personal characteristics with respect to the estimated probability of reoperations. The early reoperations were less frequent in women (4.4%, median time to reoperation 4.5 mo) than in men (8.2%, 10 mo), reaching the highest incidence in younger men (10.9%)

Introduction. The application of artificial intelligence (A.I) using patient reported outcomes (PROs) to predict benefits, risks, benefits and likelihood of improvement following surgery presents a new frontier in shared decision-making. The purpose of this study was to assess the impact of an A.I-enabled decision aid versus patient education alone on decision quality in patients with knee OA considering total knee replacement (TKR). Secondarily we assess impact on shared decision-making, patient satisfaction, functional outcomes, consultation time, TKR rates and treatment concordance. Methods. We performed a randomized controlled trial involving 130 new adult patients with OA-related knee pain. Patients were randomized to receive the decision aid (intervention group, n=65) or educational material only (control group, n=65) along with usual care. Both cohorts completed patient surveys including PROs at baseline and between 6–12 weeks following initial evaluation or TKR. Statistical analysis included linear mixed effect models, Mann-Whitney U tests to assess for differences between groups and Fisher's exact test to evaluate variations in surgical rates and treatment concordance. Results. The intervention group showed greater decision quality (K-DQI, Mean difference = 20%, p<0.0001), collaboration in decision-making (CollaboRATE, 12% (intervention group), 47% (control group) below median, p<0.0001), satisfaction with consultations (NRS-C, 14% (intervention group), 33% (control group) below median, p=0.008), improvement in functional outcomes from baseline up to 12 week follow-up (KOOSJR, 4.9 pts higher (intervention group), p=0.029) without significantly impacting consultation time. No differences were observed in TKR rates or treatment concordance. Conclusion. A.I-enabled decision aids incorporating PROs in predictive algorithms can improve decision quality, level of shared decision-making, satisfaction with patient-provider consultations, and functional outcomes, without extending consultation times. The combination of advanced predictive technologies and patient reported data to forecast surgical outcomes presents a paradigm shift in shared decision making and the delivery of high value care for patients with knee OA

Introduction. Automated identification of arthroplasty implants could aid in pre-operative planning and is a task which could be facilitated through artificial intelligence (AI) and deep learning. The purpose of this study was to develop and test the performance of a deep learning system (DLS) for automated identification and classification of knee arthroplasty (KA) on radiographs. Methods. We collected 237 AP knee radiographs with equal proportions of native knees, total KA (TKA), and unicompartmental KA (UKA), as well as 274 radiographs with equal proportions of Smith & Nephew Journey and Zimmer NexGen TKAs. Data augmentation was used to increase the number of images available for DLS development. These images were used to train, validate, and test deep convolutional neural networks (DCNN) to 1) detect the presence of TKA; 2) differentiate between TKA and UKA; and 3) differentiate between the 2 TKA models. Receiver operating characteristic (ROC) curves were generated with area under the curve (AUC) calculated to assess test performance. Results. The DCNNs trained to detect KA and to distinguish between TKA and UKA both achieved AUC of 1. In both cases, heatmap analysis demonstrated appropriate emphasis of the KA components in decision-making. The DCNN trained to distinguish between the 2 TKA models also achieved AUC of 1. Heatmap analysis of this DCNN showed emphasis of specific unique features of the TKA model designs for decision making, such as the anterior flange shape of the Zimmer NexGen TKA (Figure 1) and the tibial baseplate/stem shape of the Smith & Nephew Journey TKA (Figure 2). Conclusion. DCNNs can accurately identify presence of TKA and distinguish between specific designs. The proof-of-concept of these DCNNs may set the foundation for DCNNs to identify other prosthesis models and prosthesis-related complications. For any figures or tables, please contact the authors directly

INTRODUCTION. Statistical shape models (SSM) have become a common tool to create reference models for design input and verification of total joint implants. In a recent discussion paper around Artificial Intelligence and Machine Learning, the FDA emphasizes the importance of independent test data [1]. A leave-one-out test is a standard way to evaluate the generalization ability of an SSM [2]; however, this test does not fulfill the independence requirement of the FDA. In this study, we constructed an SSM of the knee (femur and tibia). Next to the standard leave-one-out validation, we used an independent test set of patients from a different geographical region than the patients used to build the SSM. We assessed the ability of the SSM to predict the shapes of knees in this independent test set. METHODS. A dataset of 82 computed tomography (CT) scans of Caucasian patients (42 male, 40 female) from 11 different geographic locations in France, Germany, Austria, Italy and Australia were used as training set to make an SSM of the femur and tibia. A leave-one-out test was performed to assess the ability of the SSM to predict shapes within the training set. A test dataset of 4 CT scans of Caucasian patients from Russia were used for the validation. The SSM was fitted onto each of the femur and tibia shapes and the root mean square error (RMSE) was measured. RESULTS. The leave-one-out tests showed that the femur and tibia SSMs were able to predict patients in the input population with an RMSE of 0.59 ± 0.1 mm (average ± standard deviation) for the femur and 0.70 ± 0.1 mm for the tibia. The validation test showed that the femur and tibia SSMs were able to predict the shapes of the Russian patients with an RMSE 0.62 ± 0.1 mm for the femur and 0.71 ± 0.1 mm for the tibia. DISCUSSION. There were no significant differences in the ability of the SSM to predict femur and tibia shapes of patients in a new geographic region compared to the ability of the SSM to predict shapes within the training set. CONCLUSIONS. Based on this study, 11 different geographic locations in France, Germany, Austria, Italy and Australia provide a complete sample of the Caucasian population. Using an independent set of CT scans is a valuable tool to further validate the generalization ability of an SSM. For any figures or tables, please contact authors directly

Introduction: The prediction of clinical and biological behavior of bone tumors plays an important role in medical tasks such as diagnosis and treatment planning. Different prognostic factors for bone tumors outcome appear to be significant predictors for making definitive diagnosis. It is well-know that different clinical, radiological and histological characteristics are included in diagnostic process. The most important task for pathologist is to determinate biological behavior. Errors in diagnosis lead to wrong therapy and treatment. It was reason to determinate scores for tumor diagnostics. Score is usually determinate using classic statistical methods such multivariate logistic regression (MVLR), but new computer tehniks, and models of artificial intelligence take a place in modern scoring systems. Recently, classifications tree analysis (CTA) and artificial neural network (ANN) models have become popular in decision-making and outcome prediction of clinical medicine, especially in oncology. This study compared the levels of accuracy of MVLR, CTA and ANN model for the prediction of bone tumor’s biological behavior. Material and method: Data from patient who had diagnosed bone tumors in Institute of pathology, School of Medicine in the period of 10 years (1995–2004) were used for analysis purposed in the study. In the analyzed date –base were 3689 biopsies with these criteria. About 24% (882 biopsies) were excluded because of missing data about radiological presentation. Consequently, data from 2807 biopsies were used for the analyses. Clinical, radiological, histological characteristics, summary 166 variables were analyzed and used to compare the levels of accuracy for the three methods of scoring. All data were inserting in Spider 2.0 enterprise date-base who assisted MSSQL server 2000. For MVLR and CTA we used SPSS 15.0 program with incorporate CTA. There are methods of multivariate analysis that allow for study of simultaneous influence of a series of independed variable on the one depended variable (biological behavior of bone tumors). The ANN model used in this study were feed-forward networks, witch were trained with a back propagation algorithm (NNSYSID-Neural Network Based System Identification Toolbox) situated in the Matlab area. We compared three models across theirs overall percentages. The best model was one with highest overall percentage. Results: From all analyzed cases 1590 (56, 6%) were males and 1217 (43, 4%) were females patients with Middle Ages 34, 1 (aged from 0–94 years). Malignant bone tumors (prime and metastatic lesions) were 1339 (47,7%) and benign 1468 (52,3%). From all (166) characteristics 11 were selected on the bases of a definitive analysis and included into scoring system. From clinical characteristics just age of patient and clinical diagnosis “cyst” were included. Next radiological presentations: Pure osteolysis, osteolysis with cortical destruction, osteolysis with soft tissue mass, mixed lytic and sclerotic lesion was statistically significant for scoring model. Histological presents of fibroblasts, giant cells with hamosiderin pigment in stromal cells and atypical stromal cells, and hondroid stromal production were important for classification. Localization in finger’s bone was included in definitive score too. Three performed scoring models showed wary high overall percentages in prediction biological behavior of bone tumors: MVLR 93, 77%, CTA 88, 2% and ANN 91, 5%. The most informative variable, rang 1 in both models of artificial intelligence was radiological criterion. For CTA it was radiological presents of lytic lesion with soft tissue mass and for ANN was combined lytic and sclerotic presentation. Conclusions: All three scoring models are very useful in prediction bone’s tumor behavior, most of them each ones had priority versus others. The most successive (overall percentage 93, 77%) was MVLR. ANN had high sensitivity (overall percentage 93, 77%) and gave ranges of variables included in score. CTA algorithm had the least overall percentage but it is very simple and figurative for interpretation

INTRODUCTION. Mechanical loosening of total hip replacement (THR) is primarily diagnosed using radiographs, which are diagnostically challenging and require review by experienced radiologists and orthopaedic surgeons. Automated tools that assist less-experienced clinicians and mitigate human error can reduce the risk of missed or delayed diagnosis. Thus the purposes of this study were to: 1) develop an automated tool to detect mechanical loosening of THR by training a deep convolutional neural network (CNN) using THR x-rays, and 2) visualize the CNN training process to interpret how it functions. METHODS. A retrospective study was conducted using previously collected imaging data at a single institution with IRB approval. Twenty-three patients with cementless primary THR who underwent revision surgery due to mechanical loosening (either with a loose stem and/or a loose acetabular component) had their hip x-rays evaluated immediately prior to their revision surgery (32 “loose” x-rays). A comparison group was comprised of 23 patients who underwent primary cementless THR surgery with x-rays immediately after their primary surgery (31 “not loose” x-rays). Fig. 1 shows examples of “not loose” and “loose” THR x-ray. DenseNet201-CNN was utilized by swapping the top layer with a binary classifier using 90:10 split-validation [1]. Pre-trained CNN on ImageNet [2] and not pre-trained CNN (initial zero weights) were implemented to compare the results. Saliency maps were implemented to indicate the importance of each pixel of a given x-ray on the CNN's performance [3]. RESULTS. Fig. 2 shows the saliency maps for an example x-ray and the corresponding accuracy of the CNN on the entire validation dataset at different stages of the training for both pre-trained (Fig. 2a) and not pre-trained (Fig. 2b) CNNs. Colored regions in the saliency maps, where red denotes higher relative influence than blue, indicate the most influential regions on the CNN's performance. Pre-trained CNN achieved higher accuracy (87%) on the validation set x-rays than not pre-trained CNN (62%) after 10 epochs. The pre-trained CNN's saliency map at 10 epochs identified significant influence of bone-implant interaction regions on the CNN's performance. This indicates that the CNN is ‘looking’ at the clinically relevant features in the x-rays. The saliency maps also demonstrated that the pre-trained CNN quickly learned where to ‘look’, while the not pre-trained CNN struggles. DISCUSSION. An automated tool to detect mechanical loosening of THR was developed that can potentially assist clinicians with accurate diagnosis. By visualizing the influential regions of the x-ray on the CNN performance, this study shed light into CNN learning process and demonstrated that CNN is ‘looking’ at the clinically relevant features to classify the x-rays. This visualization is crucial to build trust in the automated system by interpreting how it functions to increase the confidence in the application of artificial intelligence to the field of orthopaedics. This study also demonstrated that pre-training CNN can accelerate the learning process and achieve high accuracy even on a small dataset. For any figures or tables, please contact the authors directly

Introduction. Next generation sequencing (NGS) has been shown to facilitate detection of microbes in a clinical sample, particularly in the setting of culture-negative periprosthetic joint infection (PJI). However, it is unknown whether every microbial DNA signal detected by NGS is clinically relevant. This multi-institutional study was conceived to 1) identify species detected by NGS that may predict PJI, then 2) build a predictive model for PJI in a developmental cohort; and 3) validate the predictive utility of the model in a separate multi-institutional cohort. Methods. This multicenter investigation involving 15 academic institutions prospectively collected samples from 194 revision total knee arthroplasties (TKA) and 184 revision hip arthroplasties (THA) between 2017–2019. Patients undergoing reimplantation or spacer exchange procedures were excluded. Synovial fluid, deep tissue and swabs were obtained at the time of surgery and shipped to MicrogenDx (Lubbock, TX) for NGS analysis. Deep tissue specimens were also sent to the institutional labs for culture. All patients were classified per the 2018 Consensus definition of PJI. Microbial DNA analysis of community similarities (ANCOM) was used to identify 17 candidate bacterial species out of 294 (W-value >50) for differentiating infected vs. noninfected cases. Logistic Regression with LASSO model selection and random forest algorithms were then used to build a model for predicting PJI. For this analysis, ICM classification was the response variable (gold standard) and the species identified through ANCOM were the predictor variables. Recruited cases were randomly split in half, with one half designated as the training set, and the other half as the validation set. Using the training set, a model for PJI diagnosis was generated. The optimal resulting model was then tested for prediction ability with the validation set. The entire model-building procedure and validation was iterated 1000 times. From the model set, distributions of overall assignment rate, specificity, sensitivity, positive predictive value (PPV) and negative predicative value (NPV) were assessed. Results. The overall predictive accuracy achieved in the model was 75.9% (Figure 1). There was a high accuracy in true-negative and false-negative classification of patients using this predictive model (Figure 2), which has previously been a criticism of NGS interpretation and reporting. Specificity was 97.1%, PPV was 75.0%, and NPV was 76.2%. On comparison of the distribution of abundances between ICM-positive and ICM-negative patients, Staphylococcus aureus was the strongest contributor (F=0.99) to the predictive power of the model (Figure 3). In contrast, Cutibacterium acnes was less predictive (F=0.309) and noted to be abundant across both infected and noninfected revision TJA samples. Discussion. This study is the first to utilize predictive modeling algorithms on a large prospective multicenter database in order to transform analytic NGS data into a clinically relevant diagnostic signal. Our collaborative findings suggest the microbial DNA signal identified on NGS may be an independent useful adjunct for the diagnosis of PJI, as well as help identify causative organisms. Further work applying artificial intelligence tools will improve accuracy, predictive power and clinical utility of high-throughput sequencing technology. For figures, tables, or references, please contact authors directly

In the past measurement of deformity correction in spinal surgery has been done using measurements made directly from radiographs using a pencil, ruler and goniometer The aim of this paper is to describe a reproducible, accurate and partially automated system that has been developed for measuring x-rays of patients with spinal disorders. Computer assisted measurement of polyethylene wear in patients with THJR is now well established. Many of the image processing algorithms have been modified to allow identification of the outline of both thoracic and lumbar vertebral bodies on digital images of radiographs made from patients with spinal disorders. The Genetic Algorithm (GA), a branch of Artificial Intelligence, has been adapted to allow the modelling of a four sided figure to each vertebral body, with minimal user input. The accurate identification of each vertebral body within a spinal radiograph allows measurement of multiple parameters, including Cobb angles, vertebral width, vertebral height and cross sectional area, as well as measurement of average disc height and cross sectional area. The method is 100% reproducible for each digital image. An attempt to measure accuracy has not been made because these are two dimensional measurements of a three-dimensional structure. Comparison of these measurements between pre and post-operative radiographs for a patient allows accurate and reproducible measurement of reconstructive surgery for scoliosis and other spinal disorders. It may aid in development of a classification system for scoliosis

Surgical instrumentation for total knee arthroplasty has improved the accuracy, reproducibility and reliability of the procedure. In recent years, minimally invasive surgery introduced instrumentation that was reduced in size to fit within the smaller operative field; with this move the impact and influence of technology became proportionately larger. The introduction of computer navigation is an attempt to improve the surgeon’s visibility in a limited operative field, improve the position of the resection guides, and ultimately the position of the final components. While it may be appealing to rely on computer navigation to perform a TKA, it is not artificial intelligence and does not make any of the surgical decisions. The procedure still is surgeon directed with navigation serving as a tool of confirmation with the potential for improvements in surgical accuracy and reproducibility. The accuracy of TKA has always been dependent upon the surgeon’s judgment, experience, ability to integrate images, utilize pre-operative radiographs, knowledge of anatomic landmarks, knowledge of knee kinematics, and hand eye co-ordination. Recent advances in medical imaging, computer vision and patient specific instrumentation have provided enabling technologies, which in a synergistic manner optimize the accurate performance of the surgery. The successful use of this technology requires that it not replace the surgeon, but support the surgeon with enhanced intra-operative feedback, integration of pre-operative and intra-operative information, and visual dexterity during the procedure. In developing smart tools or robotic systems, the technology must be: safe; accurate; compatible with the operative field in size and shape, as well be able to be sterilized; and must show measurable benefits such as reduced operative time, reduced surgical trauma and improved clinical outcomes. Advocates believe this is attainable and robotic assisted TKA can achieve levels of accuracy, precision and safety not accomplished by computer assisted surgery. Smart instruments and robotic surgery are helping us take the next step into the operating room of the future. The role of robots in the operating room has the potential to increase as technology improves and appropriate applications are defined. Joint replacement arthroplasty may benefit the most due to the need for high precision in placing instruments, aligning the limb and implanting components. In addition, this technology will reduce the number of instruments needed for the procedure potentially further improving efficiency in the operating room. As technology advances, robots may be commonplace in the surgical theater and potentially transform the way total knee arthroplasty is done in the future. Robotic surgery and smart tools are new innovative technologies and it will remain to be seen if history will look on its development as a profound improvement in surgical technique or a bump on the road to something more important

Early migration of the acetabular and femoral component after total hip replacement has shown to be a good predictor of implant failure. The only current technique available for this measurement is RSA. An entirely new technique for the measurement of component migration and polyethylene wear has been developed. Required are a single CT of the patients’ pelvis and femur, and routine serial postoperative antero-posterior (AP) and lateral radiographs. A CT scan of the patients pelvis and proximal femur is performed either pre or post-operatively. This CT is used to build a solid model of the patients’ bony anatomy. CAD models of the femoral and acetabular component are obtained from the manufacturer and all four solid models are imported into custom software. Ray tracer (RT) technology is the computer generation of images of a solid model placed between a camera and a screen. It has been adapted to reproduce the radiological setup used to take clinical AP and lateral radiographs. The four solid models (pelvis, acetabular component, femoral component, femoral shaft) are each placed in the RT. Manipulation of each solid model is performed (6 degrees of freedom, x, y, z translation, and rotation about the x, y, z axis) using Artificial Intelligence, until an outline of the solid model generated by the ray tracer is identical to the outline of the AP and lateral radiograph of that patient. Change in relative positions of each solid model over time (pelvis acetabular component represents acetabular migration, acetabular component femoral stem represents polyethylene wear, and femoral stem femur represents femoral migration) are recorded. Validation to measure accuracy of the technique has been performed using computer models, and femoral and acetabular prostheses implanted into a cadaver. Despite significant variations in the position of the pelvis and leg during the obtaining of post-operative radiographs, this new technique was able to measure polyethylene wear and component migration with accuracy similar to that of RSA (0.25 mm in the AP plane). Further testing and validation is required, but this technique offers promise for the future in being able to retrospectively measure component migration and poly-ethylene wear, using a single CT scan and routine clinical postoperative radiographs

Measurement of polyethylene (PE) wear in total hip joint replacement (THJR) is performed by measuring change in the position of the femoral head on post-operative radiographs. Early methods used manual measurement with calipers and concentric circles, while more recent techniques involve the use of computer assisted technology. RSA, while mainly used for measuring component migration, can also be used for measurement of PE wear. The aim of this paper is to describe two new methods for measuring PE wear;. A completely automated measurement (which eliminates user error and is 100% reproducible). A method currently under development which uses artificial intelligence to match CAD models to radiographs, enabling measurement of both PE wear and prosthesis migration. For the Automated Measurement Technique (AMT), software has been developed which locates the centre of the acetabular cup and femoral head on both the anteroposterior and lateral radiographs. No user input is required. Accuracy is ± 0.16 mm. Clinically, it has been used in a double-blinded randomized controlled trial (RCT) comparing conventional with cross-linked PE. For the Model Matching Technique (MMT), two pieces of software are combined, Ray-Tracing technology (used in the generation of animated movies), and the Genetic Algorithm (a branch of Artificial Intelligence). CAD models of an acetabular cup and femoral head are matched to post-operative films to position them in 3D space. Change in position of these models over time represents PE wear. CAD models of the patients’ pelvis and femur (built from CT scans) can be similarly used to measure femoral and acetabular component migration. The AMT was used to measure the PE wear of 116 patients enrolled in a prospective RCT comparing conventional and cross-linked PE. At a follow-up of two to four years, cross-linked PE showed statistic ally significant lower PE wear than the conventional material. A cadaver pelvis and femur has been used to analyse accuracy of the MMT for measurement of component migration. Preliminary results show an accuracy of ± 0.22mm for component migration. The accuracy of PE wear measurement appears to be significantly less than this. The development of new bearing surfaces to reduce wear in THJR requires new techniques of in-vivo wear measurement. These two new techniques should give important information on the performance of new bearings, and possibly allow measurement of clinical component migration without the need for bead implantation

The June 2024 Hip & Pelvis Roundup³⁶⁰ looks at: Machine learning did not outperform conventional competing risk modelling to predict revision arthroplasty; Unravelling the risks: incidence and reoperation rates for femoral fractures post-total hip arthroplasty; Spinal versus general anaesthesia for hip arthroscopy: a COVID-19 pandemic- and opioid epidemic-driven study; Development and validation of a deep-learning model to predict total hip arthroplasty on radiographs; Ambulatory centres lead in same-day hip and knee arthroplasty success; Exploring the impact of smokeless tobacco on total hip arthroplasty outcomes: a deeper dive into postoperative complications.

Aims

To identify unanswered questions about the prevention, diagnosis, treatment, and rehabilitation and delivery of care of first-time soft-tissue knee injuries (ligament injuries, patella dislocations, meniscal injuries, and articular cartilage) in children (aged 12 years and older) and adults.

Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

Aims

Biofilm infections are among the most challenging complications in orthopaedics, as bacteria within the biofilms are protected from the host immune system and many antibiotics. Halicin exhibits broad-spectrum activity against many planktonic bacteria, and previous studies have demonstrated that halicin is also effective against Staphylococcus aureus biofilms grown on polystyrene or polypropylene substrates. However, the effectiveness of many antibiotics can be substantially altered depending on which orthopaedically relevant substrates the biofilms grow. This study, therefore, evaluated the activity of halicin against less mature and more mature S. aureus biofilms grown on titanium alloy, cobalt-chrome, ultra-high molecular weight polyethylene (UHMWPE), devitalized muscle, or devitalized bone.

Aims

Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds.

The December 2023 Research Roundup³⁶⁰ looks at: Tissue integration and chondroprotective potential of acetabular labral augmentation with autograft tendon: study of a porcine model; The Irish National Orthopaedic Register under cyberattack: what happened, and what were the consequences?; An overview of machine learning in orthopaedic surgery: an educational paper; Beware of the fungus…; New evidence for COVID-19 in patients undergoing joint replacement surgery.

The April 2023 Research Roundup³⁶⁰ looks at: Ear protection for orthopaedic surgeons?; Has arthroscopic meniscectomy use changed in response to the evidence?; Time to positivity of cultures obtained for periprosthetic joint infection; Bisphosphonates for post-COVID-19 osteonecrosis of the femoral head; Missing missed fractures: is AI the answer?; Congenital insensitivity to pain and correction of the knee; YouTube and paediatric elbow injuries.

Recent publications have drawn attention to the fact that some brands of joint replacement may contain variants which perform significantly worse (or better) than their ‘siblings’. As a result, the National Joint Registry has performed much more detailed analysis on the larger families of knee arthroplasties in order to identify exactly where these differences may be present and may hitherto have remained hidden. The analysis of the Nexgen knee arthroplasty brand identified that some posterior-stabilized combinations have particularly high revision rates for aseptic loosening of the tibia, and consequently a medical device recall has been issued for the Nexgen ‘option’ tibial component which was implicated. More elaborate signal detection is required in order to identify such variation in results in a routine fashion if patients are to be protected from such variation in outcomes between closely related implant types.

Cite this article: Bone Joint J 2023;105-B(3):221–226.

The June 2024 Research Roundup³⁶⁰ looks at: Do the associations of daily steps with mortality and incident cardiovascular disease differ by sedentary time levels?; Large-scale assessment of ChatGPT in benign and malignant bone tumours imaging report diagnosis and its potential for clinical applications; Long-term effects of diffuse idiopathic skeletal hyperostosis on physical function: a longitudinal analysis; Effect of intramuscular fat in the thigh muscles on muscle architecture and physical performance in the middle-aged females with knee osteoarthritis; Preoperative package of care for osteoarthritis an opportunity not to be missed?; Superiority of kinematic alignment over mechanical alignment in total knee arthroplasty during medium- to long-term follow-up: a meta-analysis and trial sequential analysis.

Benefits of early stabilization of femoral shaft fractures, in mitigation of pulmonary and other complications, have been recognized over the past decades. Investigation into the appropriate level of resuscitation, and other measures of readiness for definitive fixation, versus a damage control strategy have been ongoing. These principles are now being applied to fractures of the thoracolumbar spine, pelvis, and acetabulum. Systems of trauma care are evolving to encompass attention to expeditious and safe management of not only multiply injured patients with these major fractures, but also definitive care for hip and periprosthetic fractures, which pose a similar burden of patient recumbency until stabilized. Future directions regarding refinement of patient resuscitation, assessment, and treatment are anticipated, as is the potential for data sharing and registries in enhancing trauma system functionality.

Cite this article: Bone Joint J 2023;105-B(4):361–364.

The February 2023 Knee Roundup³⁶⁰ looks at: Machine-learning models: are all complications predictable?; Positive cultures can be safely ignored in revision arthroplasty patients that do not meet the 2018 International Consensus Meeting Criteria; Spinal versus general anaesthesia in contemporary primary total knee arthroplasty; Preoperative pain and early arthritis are associated with poor outcomes in total knee arthroplasty; Risk factors for infection and revision surgery following patellar tendon and quadriceps tendon repairs; Supervised versus unsupervised rehabilitation following total knee arthroplasty; Kinematic alignment has similar outcomes to mechanical alignment: a systematic review and meta-analysis; Lifetime risk of revision after knee arthroplasty influenced by age, sex, and indication; Risk factors for knee osteoarthritis after traumatic knee injury.

The October 2023 Hip & Pelvis Roundup³⁶⁰ looks at: Femoroacetabular impingement syndrome at ten years – how do athletes do?; Venous thromboembolism in patients following total joint replacement: are transfusions to blame?; What changes in pelvic sagittal tilt occur 20 years after total hip arthroplasty?; Can stratified care in hip arthroscopy predict successful and unsuccessful outcomes?; Hip replacement into your nineties; Can large language models help with follow-up?; The most taxing of revisions – proximal femoral replacement for periprosthetic joint infection – what’s the benefit of dual mobility?

The August 2023 Hip & Pelvis Roundup³⁶⁰ looks at: Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty; Antibiotic length in revision total hip arthroplasty; Preoperative colonization and worse outcomes; Short stem cemented total hip arthroplasty; What are the outcomes of one- versus two-stage revisions in the UK?; To cement or not to cement? The best approach in hemiarthroplasty; Similar re-revisions in cemented and cementless femoral revisions for periprosthetic femoral fractures in total hip arthroplasty; Are hip precautions still needed?

The October 2023 Children’s orthopaedics Roundup³⁶⁰ looks at: Outcomes of open reduction in children with developmental hip dislocation: a multicentre experience over a decade; A torn discoid lateral meniscus impacts lower-limb alignment regardless of age; Who benefits from allowing the physis to grow in slipped capital femoral epiphysis?; Consensus guidelines on the management of musculoskeletal infection affecting children in the UK; Diagnosis of developmental dysplasia of the hip by ultrasound imaging using deep learning; Outcomes at a mean of 13 years after proximal humeral fracture during adolescence; Clubfeet treated according to Ponseti at four years; Controlled ankle movement boot provides improved outcomes with lower complications than short leg walking cast.

The April 2023 Wrist & Hand Roundup³⁶⁰ looks at: MRI-based classification for acute scaphoid injuries: the OxSMART; Deep learning for detection of scaphoid fractures?; Ulnar shortening osteotomy in adolescents; Cost-utility analysis of thumb carpometacarpal resection arthroplasty; Arthritis of the wrist following scaphoid fracture nonunion; Extensor hood injuries in elite boxers; Risk factors for reoperation after flexor tendon repair; Nonoperative versus operative treatment for displaced finger metacarpal shaft fractures.

Aims

To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA.

Aims

It is important to analyze objectively the hammering sound in cup press-fit technique in total hip arthroplasty (THA) in order to better understand the change of the sound during impaction. We hypothesized that a specific characteristic would present in a hammering sound with successful fixation. We designed the study to quantitatively investigate the acoustic characteristics during cementless cup impaction in THA.

Aims

It is important to analyze objectively the hammering sound in cup press-fit technique in total hip arthroplasty (THA) in order to better understand the change of the sound during impaction. We hypothesized that a specific characteristic would present in a hammering sound with successful fixation. We designed the study to quantitatively investigate the acoustic characteristics during cementless cup impaction in THA.

Aims

The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients.

Aims

A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.

The unparalleled events of the year 2020 continue to evolve and challenge the worldwide community on a daily basis. The COVID-19 pandemic has had a major impact on all aspects of our lives, and has caused major morbidity and mortality around the globe. The impact of COVID-19 on the practice of orthopedic surgery has been substantial with practice shutdowns, elective surgery restrictions, heightened utilization of telemedicine platforms and implementation of precautionary measures for in-person clinic visits. During this transition period the scholarly and educational pursuits of academic surgeons have been de-emphasized as the more immediate demands of clinical practice survivorship have been the priority. This unavoidable focus on clinical practice has heightened the importance of orthopedic subspecialty societies in maintaining an appropriate level of attention on research and educational activities. Under the outstanding presidential leadership of Robert Barrack, MD, The Hip Society adapted to the profound challenges of 2020, and maintained strong leadership in the realms of education and research. The recent 2020 summer meeting of the Hip Society was a testimonial to the resilience and dedication of the Society members to ongoing innovation in research and education. Due to travel and social distancing restrictions the 2020 summer meeting was transitioned from an in-person to a virtual meeting format. Dr Barrack and Program Chair Dr John Clohisy assisted with oversight of the meeting, while Olga Foley and Cynthia Garcia ensured the success of the meeting with remarkable planning and organization. These collaborative efforts resulted in an organized, well-attended, high level scientific meeting with engaging discussion and a remarkable virtual conference environment. The Bone & Joint Journal is very pleased to partner with The Hip Society to publish the proceedings of this very unique virtual meeting. The Hip Society is based in the United States and membership is granted to select individuals for leadership accomplishments in education and research related to hip disease. The Society is focused on the mission of advancing the knowledge and treatment of hip disorders to improve the lives of patients. The vision of the Hip Society is to lead in the discovery and dissemination of knowledge related to disorders of the hip. The annual closed meeting is one of the most important events of the society as this gathering highlights timely, controversial and novel research contributions from the membership. The top research papers from The Hip Society meeting will be published and made available to the wider orthopedic community in The Bone & Joint Journal. This partnership with The Bone & Joint Journal enhances the mission and vision of The Hip Society by international dissemination of the meeting proceedings. Given the far-reaching circulation of The Bone & Joint Journal the highest quality work is available to an expanding body of surgeons, associated healthcare providers and patients. Ultimately, this facilitates the overarching Hip Society goal of improving the lives of our patients. The 2020 virtual Hip Society meeting was characterized by outstanding member attendance, high quality paper presentations and robust discussion sessions. The meeting was held over two days and encompassed 58 open paper presentations divided into ten sessions with moderated discussions after each session. All papers will be presented in this issue in abstract form, while selected full papers passing our rigorous peer review process will be available online and in The Bone & Joint Journal in a dedicated supplement in 2021. The first session of the meeting focused on issues related to complex primary THA and osteonecrosis of the femoral head. Dr Gross presented on the conversion of hip fusion to THA in 28 patents at a mean 7 years. He reported a high clinical success rate, yet complications of heterotopic ossification and neurologic injury were relatively common. Consideration of heterotopic ossification prophylaxis and the selective use of a constrained liner were recommended. Dr Pagnano summarized the use of various contemporary porous acetabular components in 38 hips in the setting of prior pelvic radiation. The mean follow-up was 5 years and 10 year survivorship was 100% with all implants radiographically fixed. Dr Bolognesi's study demonstrated that THA in solid organ transplant patients is associated with higher risk for facility placement, transfusions and readmissions. This patient population also has increased mortality risk (4.3% risk at 1 year) especially lung transplant patients. The second group of papers focused on femoral head osteonecrosis. Dr Iorio presented single center data demonstrating that CT scan was a useful adjunct for diagnosis in the staging work-up for cancer, yet was not useful for ARCO staging and treatment decision-making. On the basic science side, Dr Goodman utilized a rabbit model of steroid-induced femoral head osteonecrosis to determine that immunomodulation with IL-4 has the potential to improve bone healing after core decompression. The session was concluded by Dr Nelson's study of ceramic-on-ceramic THA in 108 osteonecrosis patients. The median 12 year results were outstanding with marked increases in PROs, maintenance of high activity levels, and a 3.7% revision rate. In the second session attention was directed to THA instability and spinopelvic mobility. Dr Sierra presented a machine learning algorithm for THA dislocation risk. Two modifiable variables (anterior/lateral approach, elevated liner) were most influential in minimizing dislocation risk. Dr Taunton's study demonstrated a deep learning artificial intelligence model derived from postoperative radiographs to predict THA dislocation risk. High sensitivity and negative predictive value suggest that this model may be helpful in assessing postoperative dislocation risk. In reviewing a large single-center, multiple surgeon cohort of 2,831 DAA procedures, Dr Moskal noted a very low dislocation rate (0.45%) at minimum 2 years. Importantly, spinopelvic pathology or prior spinal instrumentation was not associated with an increased dislocation risk (0.30%). Dr Huo and colleagues analyzed pelvic tilt during functional gait in patients with acetabular dysplasia. They detected variable pelvic tilt on different surfaces with the data suggesting that patients with more anterior pelvic tilt while standing tend to have greater compensatory posterior pelvic tilt during gait. Dr Lamontagne reported on the sagittal and axial spinomobility in patients with hip OA, and highlighted reductions in pelvic tilt, pelvic-femoral-angle, lumbar lordosis and seated maximal trunk rotation when compared to controls. Dr Dennis showed that differences in spinopelvic mobility may explain the variable accuracy of acetabular version measurements on the cross-table lateral radiographs. Dr Gwo-Chin presented on a comprehensive functional analysis of 1,592 patients undergoing THA and observed that spinopelvic abnormalities are not infrequent (14%) in THA patients. Consistent with these findings Dr Murphy and collaborators identified a low prevalence of previous spinal instrumentation (1.5%), yet a high prevalence of spine stiffness (27.6%) in 149 patients undergoing THA. Session three highlighted various aspects of treating hip disease in young patients. Dr Peters investigated the need for subsequent hip arthroscopy in 272 patients treated with an isolated PAO. Only 4.8% of these patients required subsequent arthroscopy calling into question the routine use of combined arthroscopy and PAO. Three papers addressed questions related to THA in young patients. Dr Berend's study of 2532 hips demonstrated that high activity level was not associated with an increased risk of midterm aseptic or all cause failure. Dr Nunley presented on 43 young patients with an average age of 52 years treated with a cementless stem and modular dual mobility articulation. Stress shielding was minimal and no concerning metal ion release detected. Dr Garvin summarized minimum 15 year data of THA with highly cross-linked polyethylene in patient less than 50 years. These hips performed exceptionally well with no mechanical loosening or radiographic osteolysis. Dr Engh examined 10 year results of the Birmingham Hip Resurfacing implant and reported a 92.9 % overall survivorship, with males less than 55 years achieving a 98.3% survivorship. The session was concluded by long-term data on the Conserve Plus hip resurfacing arthroplasty. Dr Amstutz presented an impressive dataset depicting an 83.1% 20 year survivorship for this early resurfacing cohort. Direct anterior approach total hip arthroplasty was the focus of session four. Dr Meneghini reported on the anesthesia and surgical times of direct anterior and posterior approaches from a large healthcare system database. These data suggested longer OR and surgical times for the DAA both in the inpatient and ASC environments. Dr Clohisy introduced the technique and early outcomes of lateral decubitus position DAA. In a learning curve experience of 257 hips. 96% of acetabular components were in the Lewinneck safe zone, the aseptic revision rate was 0.9% and there were no dislocations. Dr Beaule analyzed femoral stem cement mantle with the DAA and posterior approaches by comparing two matched cohorts. Stem alignment and cement mantle quality were equivalent with both approaches. Similarly, Dr Emerson demonstrated technical feasibility and fewer cemented femoral stem failures when compared to cementless stems in a series of 360 DAAs THAs. The final paper of the session presented by Dr Hamilton examined the impact of surgical approach on dislocation after isolated head and liner exchange. Neither the posterior nor the anterior approach was superior in reducing the dislocation rate for these high dislocation risk procedures. The fifth session explored contemporary topics related to anesthesia and pain management. Dr Byrd opened the session with a comparative study evaluating general versus spinal anesthesia for hip arthroscopy. This preliminary study was provoked by the desire to minimize aerosolized exposure early in the COVID-19 pandemic by transitioning to spinal anesthesia. Both anesthetic methods were effective. Dr Austin presented a randomized, double-blind controlled trial comparing spinal anesthetic with mepivacaine, hyperbaric bupivacaine and isobaric bupivacaine. Mepivacaine patients ambulated earlier and were more likely to be discharged the same day. Dr Mont provided a very timely study on the effects of “cannabis use disorder” and THA outcomes. This administrative database study of 44,154 patients revealed this disorder to be associated with longer hospital stays, increased complications rates and higher costs. Dr Bedair investigated whether a highly porous acetabular component submerged in an analgesic solution could enhance perioperative pain management. Interestingly, this novel strategy was associated with a reduction of postoperative pain scores and opioid consumption in 100 experimental patients compared to 100 controls. The concluding paper of the session by Dr Della Valle examined whether decreased discharge opioids led to increased postoperative opioid refills. A large single-center study of 19,428 patients detected a slight increase (5%) in opioid refills but a reduction in total refill morphine milligram equivalents. The final, sixth session of day one considered various challenging aspects of revision hip arthroplasty. Dr Nam started the session with review of preliminary results from a randomized control trial comparing closed incision negative-pressure therapy with a silver-impregnated dressing for wound management in 113 hips undergoing revision arthroplasty. Unlike previous reports, the negative pressure therapy was associated with a higher reoperation rate for wound-related complications. Dr Bostrom highlighted the potential clinical impact of basic biological interventions by establishing the presence of Neutrophil Extracellular Traps (NETS) in fibrotic tissue from human aseptic loosening specimens and in a murine model of unstable tibial implantation. NET inhibition in the murine model prevented the expected tibial implant osseointegration failure. Dr Lombardi presented early 3.3 year clinical results of a highly porous Ti6al4v acetabular component in complex primary and revision arthroplasty. Survivorship for aseptic loosening was 96.6 % and 95.3% for the primary and revision cases, respectively. Dr Schwarzkopf and colleagues explored the impact of time to revision arthroplasty on clinical outcomes. Analysis of 188 revision cases revealed early revisions (less than 2 years from primary) were associated with worse outcomes, longer hospitalizations and higher reoperation rates. Mid-term results for modular dual mobility implants in revision arthroplasty were reviewed by Dr Lachiewicz who reported on 126 hips at a mean 5.5 years. 11% of hips dislocated and the 6 year survival was 91%. An outer head diameter of 48mm or greater was associated with a lower risk of dislocation. Dr Berry concluded the session by discussing the outcomes of treating the challenging problem of interprosthetic femur fractures. A single-center study of 77 cases treated over 32 years demonstrated a 79% success rate free of reoperation at 2 years with 95% of patients being ambulatory. The second day commenced with the seventh session evaluating recent strategies to improve short-term THA outcomes. Dr Bozic and colleagues investigated the association of quality measure public reporting with hip/knee replacement outcomes. Annual trend data from 2010–2011 and 2016–2017 indicate that hospital-level complication and readmission rates decease after the start of public reporting, yet it is difficult to prove a direct effect. Dr Slover reviewed his institutions experience with the Comprehensive Care for Joint Replacement (CJR) model and emphasized that lower CJR target prices make it increasingly difficult for programs to meet target price thresholds. Cost saving strategies including same day discharge and reduction of home health services may result in smaller losses of positive margins. Dr Barsoum reported on the influence of patient and procedure-related risk factors of length of stay after THA. Patient-related risk factors provided substantial predictive value yet procedure-related risk factors (hospital site and surgical approach) remain the main drivers of predicting length of stay. Dr Hozack reviewed an impressive, single surgeon cohort of 3,977 DAA THAs and analyzed adverse events and 90 day perioperative outcomes. Simultaneous bilateral DAA THA was comparable with unilateral or staged bilateral procedures in regards to complications, readmission rate and home discharge rate but with an increased risk of transfusion. To examine the risk of complications with outpatient joint arthroplasty, Dr Della Valle performed a single-surgeon matched cohort analysis comparing outpatient and inpatient hip and knee arthroplasties. Outpatient procedures were not associated with an increased risk of any postoperative complications and actually experienced fewer emergency department visits. The eighth session covered various contemporary challenges in hip arthroplasty care. Dr Griffin began the session with an analysis of the timing of complications associated with two-stage exchange procedures for periprosthetic joint infection (PJI). Of the 189 hips included, 41.6% had a complication and the mortality was 14.1% at 2.5 years, highlighting the morbidity of this treatment method. Dr Fehring provided data assessing the fate of two-stage reimplantation after failed debridement, antibiotics and implant retention (DAIR) for a prosthetic hip infection. This analysis of 114 hips yielded concerning results demonstrating a 42.9% treatment failure of patients treated with a previous DAIR compared to a 12.3% failure rate in patients treated with an initial 2-stage procedure. Dr Jacobs reviewed the analysis of 106 femoral heads with severe corrosion and identified a chemically dominated etching process termed “column damage” to be a detrimental damage mode within CoCr femoral heads that is directly linked to banding within its microstructure. These data indicate that implant alloy microstructure must be optimized to minimize the release of fretting-corrosion products. Simon Mears presented retrospective data from 184 THAs with a dual modular femoral stem. A subgroup of hips with a modular, cobalt chromium femoral neck had a pseudotumor visualized in 15% with only 55% of these having elevated CoCr levels. These findings may support the use of routine follow-up MARS MRI for modular CoCr femoral neck prostheses. The final two studies explored timely issues related to viral illness and hip surgery. Dr Browne analyzed three large administrative databases to elucidate whether patients are at increased risk for viral illnesses following total joint replacement. The incidence of postoperative influenza after total joint replacement was not increased compared to patients not undergoing total joint replacement surgery suggesting that arthroplasty procedures may not heighten the risk of viral illness. In the final paper of the session Dr Haddad presented important data regarding perioperative complications in coronavirus positive patients undergoing surgical treatment of femoral neck fractures. In this multicenter cohort study from the United Kingdom 82 coronavirus positive patients were shown to have longer hospital stays, more critical care unit admissions, higher risk of perioperative complications and an increased mortality compared to 340 coronavirus negative patients. The eighth session had two papers on alternative femoral stem designs and three presentations more focused on femoral fracture treatments. Dr Mihalko focused on the European and US experiences with the Metha femoral neck retaining stem. The US experience mirrored the encouraging results from Europe with a 94% all cause femoral survivorship and a 99.1% femoral aseptic loosening survivorship at 5 years. Dr Kraay summarized dual energy x-ray absorptiometry (DEXA) evaluation of 16 low modulus composite femoral components at long-term follow-up of a mean 22 years. The bone mineral density associated with the implant increased in Gruen zones 2–6 and showed limited decreases in zones 1 and 7. These data support the concept that a low modulus femoral stem may more effectively load the proximal femur. Dr Springer provided data from the American Joint Replacement Registry (AJRR) and by evaluating outcomes of exact matched cohorts of 17,138 patients treated with cementless or cemented femoral implants for femoral neck fractures. Cemented implants were associated with marked reduction in early revision and periprosthetic fractures. However, cemented fixation was associated with an increased mortality at 90 days and 1 year. Additional data from the AJRR was presented by Dr Huddleston who investigated the risk factors for revision surgery after arthroplasty in a cohort of 75,333 femoral neck fractures. THA when compared to hemiarthroplasty was associated with higher early and overall revision rates. Cementless femoral fixation and increased age were also associated with higher rates of any revision. Both of these studies from the AJRR suggest that further consideration should be given to femoral fixation preferences in the femoral neck fracture population. Dr Vail summarized his institution's experience with an interdisciplinary hip fracture protocol for patients undergoing arthroplasty for acute femoral neck fractures. His study compared 157 cases prior to protocol implementation with 114 patients treated after the protocol was established. The impact of the interdisciplinary protocol was impressive as evidenced by a reduced time to operative treatment, length of stay, complication rate and one-year mortality. All being achieved without an increase in readmissions or facility discharges. The final session of the meeting addressed innovations in perioperative care of THA patients. Dr Barrack started the session with an interesting study examining the feasibility and patient preferences regarding telemedicine. A cross-sectional telephone survey of 163 arthroplasty patients indicated that 88% of patients use the internet and 94% own a device capable of videoconferencing. Nevertheless, only 18% of patients preferred a video visit over an in-person clinic visit due to concerns of inferior care. Dr Barnes quantified preoperative optimization work in 100 arthroplasty patients by using EMR activity logs and determined the surgical team spends an average 75 minutes per case on preoperative work activities. Dr Duwelius reported the early outcomes of primary THA with a smartphone-based exercise and educational platform compared to standard of care controls. A randomized control trial design with 365 patients demonstrated similar outcomes and non-inferiority of the smartphone platform relative to complications, readmissions, emergency room/urgent care visits. The association of controlled substance use with THA outcomes was assessed by Dr Higuera Rueda. A quantitative assessment using the NarxCare score identified 300 and above as a score associated with adverse outcomes after THA. Dr Macaulay reviewed data from a large retrospective study of 1,825 THAs indicating that discontinuation of intermittent pneumatic compression devices does not increase the risk of venous thromboembolism in standard risk patients being treated with 81mg ASA BID as prophylaxis. Dr Antoniou presented the final paper of the meeting investigating potential changes in patient health status as an indication for surgery over time. Data from this large systematic review of the literature found patients undergoing THA at similar health status to the past with no influence form patient age, gender, year of enrollment or geographic region. As summarized above, the 2020 virtual Hip Society Summer Meeting was rich in scientific content, productive discussion and a collaborative spirit. This collective body of work will result in impactful scientific contributions and will serve as a foundation for future innovation and advancements in the treatment of hip disease

Aims

To map literature on prognostic factors related to outcomes of revision total knee arthroplasty (rTKA), to identify extensively studied factors and to guide future research into what domains need further exploration.

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Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction.

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The aim of this study was to systematically compare the safety and accuracy of robot-assisted (RA) technique with conventional freehand with/without fluoroscopy-assisted (CT) pedicle screw insertion for spine disease.

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This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA).

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The ongoing COVID-19 pandemic has disrupted and delayed medical and surgical examinations where attendance is required in person. Our article aims to outline the validity of online assessment, the range of benefits to both candidate and assessor, and the challenges to its implementation. In addition, we propose pragmatic suggestions for its introduction into medical assessment.

Aims

Optimal exposure through the direct anterior approach (DAA) for total hip arthroplasty (THA) conducted on a regular operating theatre table is achieved with a standardized capsular releasing sequence in which the anterior capsule can be preserved or resected. We hypothesized that clinical outcomes and implant positioning would not be different in case a capsular sparing (CS) technique would be compared to capsular resection (CR).