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. 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.Aims
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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.
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
Aims. Knee osteoarthritis (OA) involves a variety of tissues in the joint. Gene expression profiles in different tissues are of great importance in order to understand OA. Methods. First, we obtained gene expression profiles of cartilage, synovium, subchondral bone, and meniscus from the Gene Expression Omnibus (GEO). Several datasets were standardized by merging and removing batch effects. Then, we used
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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:
The aim of this study was to determine the effectiveness of home-based prehabilitation on pre- and postoperative outcomes in participants awaiting total knee (TKA) and hip arthroplasty (THA). A systematic review with meta-analysis of randomized controlled trials (RCTs) of prehabilitation interventions for TKA and THA. MEDLINE, CINAHL, ProQuest, PubMed, Cochrane Library, and Google Scholar databases were searched from inception to October 2022. Evidence was assessed by the PEDro scale and the Cochrane risk-of-bias (ROB2) tool.Aims
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An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data.Aims
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The February 2023 Knee Roundup. 360. 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
Factors associated with high mortality rates in geriatric hip fracture patients are frequently unmodifiable. Time to surgery, however, might be a modifiable factor of interest to optimize clinical outcomes after hip fracture surgery. This study aims to determine the influence of postponement of surgery due to non-medical reasons on clinical outcomes in acute hip fracture surgery. This observational cohort study enrolled consecutively admitted patients with a proximal femoral fracture, for which surgery was performed between 1 January 2018 and 11 January 2021 in two level II trauma teaching hospitals. Patients with medical indications to postpone surgery were excluded. A total of 1,803 patients were included, of whom 1,428 had surgery < 24 hours and 375 had surgery ≥ 24 hours after admission.Aims
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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:
Aims. We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. Methods. Five datasets were obtained from the Gene Expression Omnibus database.
The aim of this study was to review the current evidence surrounding curve type and morphology on curve progression risk in adolescent idiopathic scoliosis (AIS). A comprehensive search was conducted by two independent reviewers on PubMed, Embase, Medline, and Web of Science to obtain all published information on morphological predictors of AIS progression. Search items included ‘adolescent idiopathic scoliosis’, ‘progression’, and ‘imaging’. The inclusion and exclusion criteria were carefully defined. Risk of bias of studies was assessed with the Quality in Prognostic Studies tool, and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. In all, 6,286 publications were identified with 3,598 being subjected to secondary scrutiny. Ultimately, 26 publications (25 datasets) were included in this review.Aims
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Total hip arthroplasties (THAs) are performed by surgeons at various stages in training with varying levels of supervision, but we do not know if this is safe practice with comparable outcomes to consultant-performed THA. Our aim was to examine the association between surgeon grade, the senior supervision of trainees, and the risk of revision following THA. We performed an observational study using National Joint Registry (NJR) data. We included adult patients who underwent primary THA for osteoarthritis, recorded in the NJR between 2003 and 2016. Exposures were operating surgeon grade (consultant or trainee) and whether or not trainees were directly supervised by a scrubbed consultant. Outcomes were all-cause revision and the indication for revision up to ten years. We used methods of survival analysis, adjusted for patient, operation, and healthcare setting factors.Aims
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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:
The purpose of this study is to evaluate early outcomes with the use of a smartphone-based exercise and educational care management system after total hip arthroplasty (THA) and demonstrate decreased use of in-person physiotherapy (PT). A multicentre, prospective randomized controlled trial was conducted to evaluate a smartphone-based care platform for primary THA. Patients randomized to the control group (198) received the institution’s standard of care. Those randomized to the treatment group (167) were provided with a smartwatch and smartphone application. PT use, THA complications, readmissions, emergency department/urgent care visits, and physician office visits were evaluated. Outcome scores include the Hip disability and Osteoarthritis Outcome Score (HOOS, JR), health-related quality-of-life EuroQol five-dimension five-level score (EQ-5D-5L), single leg stance (SLS) test, and the Timed Up and Go (TUG) test.Aims
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The purpose is to determine the non-inferiority of a smartphone-based exercise educational care management system after primary knee arthroplasty compared with a traditional in-person physiotherapy rehabilitation model. A multicentre prospective randomized controlled trial was conducted evaluating the use of a smartphone-based care management system for primary total knee arthroplasty (TKA) and partial knee arthroplasty (PKA). Patients in the control group (n = 244) received the respective institution’s standard of care with formal physiotherapy. The treatment group (n = 208) were provided a smartwatch and smartphone application. Early outcomes assessed included 90-day knee range of movement, EuroQoL five-dimension five-level score, Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) score, 30-day single leg stance (SLS) time, Time up and Go (TUG) time, and need for manipulation under anaesthesia (MUA).Aims
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To develop a core outcome set of measurements from postoperative radiographs that can be used to assess technical skill in performing dynamic hip screw (DHS) and hemiarthroplasty, and to validate these against Van der Vleuten’s criteria for effective assessment. A Delphi exercise was undertaken at a regional major trauma centre to identify candidate measurement items. The feasibility of taking these measurements was tested by two of the authors (HKJ, GTRP). Validity and reliability were examined using the radiographs of operations performed by orthopaedic resident participants (n = 28) of a multicentre randomized controlled educational trial (ISRCTN20431944). Trainees were divided into novice and intermediate groups, defined as having performed < ten or ≥ ten cases each for DHS and hemiarthroplasty at baseline. The procedure-based assessment (PBA) global rating score was assumed as the gold standard assessment for the purposes of concurrent validity. Intra- and inter-rater reliability testing were performed on a random subset of 25 cases.Aims
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Arthroplasty skills need to be acquired safely during training, yet operative experience is increasingly hard to acquire by trainees. Virtual reality (VR) training using headsets and motion-tracked controllers can simulate complex open procedures in a fully immersive operating theatre. The present study aimed to determine if trainees trained using VR perform better than those using conventional preparation for performing total hip arthroplasty (THA). A total of 24 surgical trainees (seven female, 17 male; mean age 29 years (28 to 31)) volunteered to participate in this observer-blinded 1:1 randomized controlled trial. They had no prior experience of anterior approach THA. Of these 24 trainees, 12 completed a six-week VR training programme in a simulation laboratory, while the other 12 received only conventional preparatory materials for learning THA. All trainees then performed a cadaveric THA, assessed independently by two hip surgeons. The primary outcome was technical and non-technical surgical performance measured by a THA-specific procedure-based assessment (PBA). Secondary outcomes were step completion measured by a task-specific checklist, error in acetabular component orientation, and procedure duration.Aims
Patients and Methods
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:
