Data-driven methods have transformed our ability to assess and respond to human movement with wearable robots, promising real-world rehabilitation and augmentation benefits. However, the proliferation of data-driven methods, with the associated deman...
Total hip arthroplasty (THA) is the primary treatment for end-stage hip osteoarthritis, with successful outcomes depending on precise preoperative planning that requires accurate segmentation and reconstruction of periarticular bone of the hip joint....
The overall goal of this study was to develop a computational framework to quantify hip, knee, and ankle joint forces during exoskeletal-assisted walking (EAW) in the ReWalk P6.0, an FDA-approved lower-extremity exoskeleton. The first objective was t...
Developmental dysplasia of the Hip (DDH) is a common pediatric orthopedic disease, characterized primarily by abnormal development of the hip joint structure. The clinical objective is the early detection, diagnosis, and treatment. Current diagnostic...
The American journal of sports medicine
Jun 7, 2025
BACKGROUND: Multiple 2-dimensional magnetic resonance imaging (MRI) studies have indicated that the size of the labrum adjusts in response to altered joint loading. In patients with hip dysplasia, it tends to increase as a compensatory mechanism for ...
Markerless motion capture (ML) systems, which utilize deep learning algorithms, have significantly expanded the applications of biomechanical analysis. Jump tests are now essential tools for athlete monitoring and injury prevention. However, the vali...
Radiographic imaging is typically used to diagnose osteoarthritis (OA). However, patients would typically be sent for imaging after they present to a physician because of joint pain. By this time, the condition is likely irreversible. This study aims...
OBJECTIVES: To evaluate the performance of high-resolution deep learning-based hip MR imaging (CSAI) compared to standard-resolution compressed sense (CS) sequences using hip arthroscopy as standard of reference.
Artificial neural networks (ANNs) are becoming a regular tool to support biomechanical methods, while physics-based models are widespread to understand the mechanics of body in motion. Thus, this study aimed to demonstrate the accuracy of recurrent A...
Several studies have highlighted the advantages of employing artificial intelligence (AI) models in gait analysis. However, the credibility and practicality of integrating these models into clinical gait routines remain uncertain. This study critical...
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