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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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
Methods
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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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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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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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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Cortical and cancellous bone healing processes appear to be histologically different. They also respond differently to anti-inflammatory agents. We investigated whether the leucocyte composition on days 3 and 5 after cortical and cancellous injuries to bone was different, and compared changes over time using day 3 as the baseline. Ten-week-old male C56/Bl6J mice were randomized to either cancellous injury in the proximal tibia or cortical injury in the femoral diaphysis. Regenerating tissues were analyzed with flow cytometry at days 3 and 5, using panels with 15 antibodies for common macrophage and lymphocyte markers. The cellular response from day 3 to 5 was compared in order to identify differences in how cancellous and cortical bone healing develop.Objectives
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The annual incidence of hip fracture is 620 000 in the European Union. The cost of this clinical problem has been estimated at 1.75 million disability-adjusted life years lost, equating to 1.4% of the total healthcare burden in established market economies. Recent guidance from The National Institute for Health and Clinical Excellence (NICE) states that research into the clinical and cost effectiveness of total hip arthroplasty (THA) as a treatment for hip fracture is a priority. We asked the question: can a trial investigating THA for hip fracture currently be delivered in the NHS? We performed a contemporaneous process evaluation that provides a context for the interpretation of the findings of WHiTE Two – a randomised study of THA for hip fracture. We developed a mixed methods approach to situate the trial centre within the context of wider United Kingdom clinical practice. We focused on fidelity, implementation, acceptability and feasibility of both the trial processes and interventions to stakeholder groups, such as healthcare providers and patients.Objectives
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The PROximal Fracture of the Humerus: Evaluation by Randomisation (PROFHER) trial has recently demonstrated that surgery is non-superior to non-operative treatment in the management of displaced proximal humeral fractures. The objective of this study was to assess current surgical practice in the context of the PROFHER trial in terms of patient demographics, injury characteristics and the nature of the surgical treatment. A total of ten consecutive patients undergoing surgery for the treatment of a proximal humeral fracture from each of 11 United Kingdom hospitals were retrospectively identified over a 15 month period between January 2014 and March 2015. Data gathered for the 110 patients included patient demographics, injury characteristics, mode of surgical fixation, the grade of operating surgeon and the cost of the surgical implants.Objectives
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Wear debris released from bearing surfaces has been shown to
provoke negative immune responses in the recipient. Excessive wear
has been linked to early failure of prostheses. Analysis using coordinate
measuring machines (CMMs) can provide estimates of total volumetric
material loss of explanted prostheses and can help to understand
device failure. The accuracy of volumetric testing has been debated,
with some investigators stating that only protocols involving hundreds
of thousands of measurement points are sufficient. We looked to
examine this assumption and to apply the findings to the clinical
arena. We examined the effects on the calculated material loss from
a ceramic femoral head when different CMM scanning parameters were
used. Calculated wear volumes were compared with gold standard gravimetric
tests in a blinded study. Objectives
Methods
