Introduction. Knee arthroplasty (KA), encompassing Total Knee Replacement (TKR) and Unicompartmental Knee Replacement (UKR), is one of the most common orthopedic procedures, aimed at alleviating severe knee arthritis. Postoperative KA management, especially radiographic imaging, remains a substantial financial burden and lacks standardised protocols for its clinical utility during follow-up. Method. In this retrospective multicentre cohort study, data were analysed from January 2014 to March 2020 for adult patients undergoing primary KA at Imperial NHS Trust. Patients were followed over a five-year period. Four machine learning models were developed to evaluate if post-operative X-ray frequency can predict revision surgery. The best-performing model was used to assess the risk of revision surgery associated with different number of X-rays. Result. The study assessed 289 knees with a 2.4% revision rate. The revision group had more X-rays on average than the primary group. The best performing model was Logistic Regression (LR), which indicated that each additional X-ray raised the revision risk by 52% (p<0.001). Notably, having four or more X-rays was linked to a three-fold increase in risk of revision (OR=3.02; p<0.001). Our results align with the literature that immediate post-operative X-rays have limited utility, making the 2nd post-operative X-ray of highest importance in understanding the patient's trajectory. These insights can enhance management by improving risk stratification for patients at higher revision surgery risk. Despite LR being the best-performing model, it is limited by the dataset's significant class imbalance. Conclusion. X-ray frequency can independently predict revision surgery. This study provides insights that can guide surgeons in evidence-based post-operative decision-making. To use those findings and influence post-operative management, future studies should build on this predictive model by incorporating a more robust dataset, surgical indications, and X-ray findings. This will allow early identification of high-risk patients, allowing for personalised post-operative recommendations

Introduction. Experimental bone research often generates large amounts of histology and histomorphometry data, and the analysis of these data can be time-consuming and trivial. Machine learning offers a viable alternative to manual analysis for measuring e.g. bone volume versus total volume. The objective was to develop a neural network for image segmentation, and to assess the accuracy of this network when applied to ectopic bone formation samples compared to a ground truth. Method. Thirteen tissue slides totaling 114 megapixels of ectopic bone formation were selected for model building. Slides were split into training, validation, and test data, with the test data reserved and only used for the final model assessment. We developed a neural network resembling U-Net that takes 512×512 pixel tiles. To improve model robustness, images were augmented online during training. The network was trained for 3 days on a NVidia Tesla K80 provided by a free online learning platform against ground truth masks annotated by an experienced researcher. Result. During training, the validation accuracy improved and stabilised at approx. 95%. The test accuracy was 96.1 %. Conclusion. Most experiments using ectopic bone formation will yield an inter-observer or inter-method variance of far more than 5%, so the current approach may be a valid and feasible technique for automated image segmentation for large datasets. More data or a consensus-based ground truth may improve training stability and validation accuracy. The code and data of this project are available upon request and will be available online as part of our publication

Introduction. The increased prevalence of osteoporosis in the patient population undergoing reverse shoulder arthroplasty (RSA) results in significantly increased complication rates. Mainly demographic and clinical predictors are currently taken into the preoperative assessment for risk stratification without quantification of preoperative computed tomography (CT) data (e.g. bone density). It was hypothesized that preoperative CT bone density measures would provide objective quantification with subsequent classification of the patients’ humeral bone quality. Methods. Thirteen bone density parameters from 345 preoperative CT scans of a clinical RSA cohort represented the data set in this study. The data set was divided into testing (30%) and training data (70%), latter included an 8-fold cross validation. Variable selection was performed by choosing the variables with the highest descriptive value for each correlation clustered variables. Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of bone densities at risk for complications and were compared to a conventional statistical model (Logistic Regression (LR)). Results. Clustering partitioned this cohort (training data set) into a high bone density subgroup consisting of 96 patients and a low bone density subgroup consisting of 146 patients. The optimal number of clusters (n = 2) was determined based on optimization metrics. Discrimination of the cross validated classification model showed comparable performance for the training (accuracy=91.2%; AUC=0.967) and testing data (accuracy=90.5 %; AUC=0.958) while outperforming the conventional statistical model (Logistic Regression (LR)). Local interpretable model-agnostic explanations (LIME) were created for each patient to explain how the predicted output was achieved. Conclusion. The trained and tested model provides preoperative information for surgeons treating patients with potentially poor bone quality. The use of machine learning and patient-specific calibration showed that multiple 3D bone density scores improved accuracy for objective preoperative bone quality assessment

Understanding spinopelvic mechanics is important for the success of total hip arthroplasty (THA). Despite significant advancements in appreciating spinopelvic balance, numerous challenges remain. It is crucial to recognize the individual variability and postoperative changes in spinopelvic parameters and their consequential impact on prosthetic component positioning to mitigate the risk of dislocation and enhance postoperative outcomes. This review describes the integration of advanced diagnostic approaches, enhanced technology, implant considerations, and surgical planning, all tailored to the unique anatomy and biomechanics of each patient. It underscores the importance of accurately predicting postoperative spinopelvic mechanics, selecting suitable imaging techniques, establishing a consistent nomenclature for spinopelvic stiffness, and considering implant-specific strategies. Furthermore, it highlights the potential of artificial intelligence to personalize care.

Cite this article: Bone Joint J 2024;106-B(11):1206–1215.

Aims

The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs.

Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Aims. This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously. Methods. Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes. Results. WGCNA revealed critical gene modules for OB and OP, identifying the Toll-like receptor (TLR) signalling pathway as a common factor. TLR2 was the most significant gene, with a pronounced expression in macrophages. Elevated TLR2 expression correlated with increased adipose accumulation, inflammation, and osteoclast differentiation, linking it to OP development. Conclusion. Our study underscores the pivotal role of TLR2 in connecting OP and OB. It highlights the influence of TLR2 in macrophages, driving both diseases through a pro-inflammatory mechanism. These insights propose TLR2 as a potential dual therapeutic target for treating OP and OB. Cite this article: Bone Joint Res 2024;13(10):573–587

Aims. This study explored the shared genetic traits and molecular interactions between postmenopausal osteoporosis (POMP) and sarcopenia, both of which substantially degrade elderly health and quality of life. We hypothesized that these motor system diseases overlap in pathophysiology and regulatory mechanisms. Methods. We analyzed microarray data from the Gene Expression Omnibus (GEO) database using weighted gene co-expression network analysis (WGCNA), machine learning, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify common genetic factors between POMP and sarcopenia. Further validation was done via differential gene expression in a new cohort. Single-cell analysis identified high expression cell subsets, with mononuclear macrophages in osteoporosis and muscle stem cells in sarcopenia, among others. A competitive endogenous RNA network suggested regulatory elements for these genes. Results. Signal transducer and activator of transcription 3 (STAT3) was notably expressed in both conditions. Single-cell analysis pinpointed specific cells with high STAT3 expression, and microRNA (miRNA)-125a-5p emerged as a potential regulator. Experiments confirmed the crucial role of STAT3 in osteoclast differentiation and muscle proliferation. Conclusion. STAT3 has emerged as a key gene in both POMP and sarcopenia. This insight positions STAT3 as a potential common therapeutic target, possibly improving management strategies for these age-related diseases. Cite this article: Bone Joint Res 2024;13(8):411–426

Periacetabular Osteotomy (PAO) has become the most important surgical procedure for patients with hip dysplasia, offering significant pain relief and improved joint function. This study focuses on recovery after PAO, specifically the return to sports (RTS) timeline, with the objective of identifying preoperative predictors to optimize patient outcomes. Our prospective, monocentric study from 2019 to 2023 included 698 hips from 606 patients undergoing PAO. Comprehensive preoperative data were collected, including demographic information, clinical assessments (Modified Harris Hip Score (mHHS), International Hip Outcome Tool-12 (iHot-12), Hip Disability and Osteoarthritis Outcome Score (HOOS), UCLA Activity Score) and psychological evaluations (Brief Symptom Inventory (BSI) and SF-36 Health Survey). Advanced logistic regression and machine learning techniques (R Core Team. (2016)) were employed to develop a predictive model. Multivariate regression analysis revealed that several preoperative factors significantly influenced the RTS timeline. These included gender, invasiveness of the surgical approach, preoperative UCLA Score, preoperative sports activity level, mHHS, and various HOOS subscales (Sport/Recreation, Symptoms, Pain) as well as psychological factors (BSI and SF-36). The subsequent model, using a decision tree approach, showed that the combination of a UCLA score greater than 3 (p<0.001), non-female gender (p=0.003), preoperative sports frequency not less than twice per week (p<0.001), participation in high-impact sports preoperatively (p=0.008), and a BSI anxiety score less than 2 (p<0.001) had the highest likelihood of early RTS with a probability of 71.4% at three months. Using a decision tree approach, this model provides a nuanced prediction of RTS after PAO, highlighting the synergy of physical, psychological, and lifestyle influences. By quantifying the impact of these variables, it provides clinicians with a valuable tool for predicting individual patient recovery trajectories, aiding in tailored rehabilitation planning and predicting postoperative satisfaction

Background. Magnetic resonance imaging (MRI) algorithm identifies end stage severely degenerated disc as ‘black’, and a moderately degenerate to non-degenerated disc as ‘white’. MRI is based on signal intensity changes that identifies loss of proteoglycans, water, and general radial bulging but lacks association with microscopic features such as fissure, endplate damage, persistent inflammatory catabolism that facilitates proteoglycan loss leading to ultimate collapse of annulus with neo-innervation and vascularization, as an indicator of pain. Thus, we propose a novel machine learning based imaging tool that combines quantifiable microscopic histopathological features with macroscopic signal intensities changes for hybrid assessment of disc degeneration. Methods. 100-disc tissue were collected from patients undergoing surgeries and cadaveric controls, age range of 35–75 years. MRI Pfirrmann grades were collected in each case, and each disc specimen were processed to identify the 1) region of interest 2) analytical imaging vector 3) data assimilation, grading and scoring pattern 4) identification of machine learning algorithm 5) predictive learning parameters to form an interface between hardware and software operating system. Results. Kernel algorithm defines non-linear data in xy histogram. X,Y values are scored histological spatial variables that signifies loss of proteoglycans, blood vessels ingrowth, and occurrence of tears or fissures in the inner and outer annulus regions mapped with the dampening and graded series of signal intensity changes. Conclusion. To our knowledge this study is the first to propose a machine learning method between microscopic spatial tissue changes and macroscopic signal intensity grades in the intervertebral disc. No conflict of interest declared.  . Sources of Funding. ICMR/5/4-5/3/42/Neuro/2022-NCD-1, Dr TMA PAI SMU/ 131/ REG/ TMA PURK/ 164/2020. A part of the above study was presented as an oral paper at the International Society for the Study of Lumbar Spine (ISSLS) meeting held on 1–5. th. May 2023, Melbourne, Australia

Aims

Cemented hemiarthroplasty is an effective form of treatment for most patients with an intracapsular fracture of the hip. However, it remains unclear whether there are subgroups of patients who may benefit from the alternative operation of a modern uncemented hemiarthroplasty – the aim of this study was to investigate this issue. Knowledge about the heterogeneity of treatment effects is important for surgeons in order to target operations towards specific subgroups who would benefit the most.

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.

The June 2024 Wrist & Hand Roundup³⁶⁰ 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 June 2024 Hip & Pelvis Roundup. 360. 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

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

Femoroacetabular Impingement (FAI) syndrome, characterised by abnormal hip contact causing symptoms and osteoarthritis, is measured using the International Hip Outcome Tool (iHOT). This study uses machine learning to predict patient outcomes post-treatment for FAI, focusing on achieving a minimally clinically important difference (MCID) at 52 weeks. A retrospective analysis of 6133 patients from the NAHR who underwent hip arthroscopic treatment for FAI between November 2013 and March 2022 was conducted. MCID was defined as half a standard deviation (13.61) from the mean change in iHOT score at 12 months. SKLearn Maximum Absolute Scaler and Logistic Regression were applied to predict achieving MCID, using baseline and 6-month follow-up data. The model's performance was evaluated by accuracy, area under the curve, and recall, using pre-operative and up to 6-month postoperative variables. A total of 23.1% (1422) of patients completed both baseline and 1-year follow-up iHOT surveys. The best results were obtained using both pre and postoperative variables. The machine learning model achieved 88.1% balanced accuracy, 89.6% recall, and 92.3% AUC. Sensitivity was 83.7% and specificity 93.5%. Key variables determining outcomes included MCID achievement at 6 months, baseline iHOT score, 6-month iHOT scores for pain, and difficulty in walking or using stairs. The study confirmed the utility of machine learning in predicting long-term outcomes following arthroscopic treatment for FAI. MCID, based on the iHOT 12 tools, indicates meaningful clinical changes. Machine learning demonstrated high accuracy and recall in distinguishing between patients achieving MCID and those who did not. This approach could help early identification of patients at risk of not meeting the MCID threshold one year after treatment

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.

Aims

This systematic review aims to identify 3D predictors derived from biplanar reconstruction, and to describe current methods for improving curve prediction in patients with mild adolescent idiopathic scoliosis.