Aims. To develop prediction models using machine-learning (ML)
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
Aims. There remains a lack of consensus regarding the management of chronic anterior sternoclavicular joint (SCJ) instability. This study aimed to assess whether a standardized treatment
Aims. The aim of this study was to evaluate the reliability and validity of a patient-specific
Aims. The management of mid-shaft clavicle fractures (MSCFs) has evolved over the last three decades. Controversy exists over which specific fracture patterns to treat and when. This review aims to synthesize the literature in order to formulate an appropriate management
Aims. Once knee arthritis and deformity have occurred, it is currently not known how to determine a patient’s constitutional (pre-arthritic) limb alignment. The purpose of this study was to describe and validate the arithmetic hip-knee-ankle (aHKA)
Aims. We aimed to assess the reliability and validity of OpenPose, a posture estimation
Aims. Total hip arthroplasty (THA) in patients with post-polio residual paralysis (PPRP) is challenging. Despite relief in pain after THA, pre-existing muscle imbalance and altered gait may cause persistence of difficulty in walking. The associated soft tissue contractures not only imbalances the pelvis, but also poses the risk of dislocation, accelerated polyethylene liner wear, and early loosening. Methods. In all, ten hips in ten patients with PPRP with fixed pelvic obliquity who underwent THA as per an
Aims. Fungal periprosthetic joint infections (fPJIs) are rare complications, constituting only 1% of all PJIs. Neither a uniform definition for fPJI has been established, nor a standardized treatment regimen. Compared to bacterial PJI, there is little evidence for fPJI in the literature with divergent results. Hence, we implemented a novel treatment
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Aims. 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. Methods. 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
Aims. The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales. Methods. We developed CAT
Aims. Our objective was describing an
Aims. Machine learning (ML), a branch of artificial intelligence that uses
Despite the vast quantities of published artificial intelligence (AI)
Aims. To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction
Aims. Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length. Results. The
Aims. This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Methods. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning
Aims. An
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