AIMC Topic: Hip Joint

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Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study.

BMC musculoskeletal disorders
BACKGROUND: Predicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This study aimed to develop an artificial intelligence (AI) model to predict hip OA progression via plain r...

Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in assistive technology require reference torque trajectories to set the level of assistance for a patient...

Recurrent Neural Network Enabled Continuous Motion Estimation of Lower Limb Joints From Incomplete sEMG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Decoding continuous human motion from surface electromyography (sEMG) in advance is crucial for improving the intelligence of exoskeleton robots. However, incomplete sEMG signals are prevalent on account of unstable data transmission, sensor malfunct...

Artificial Intelligence Applications in MR Imaging of the Hip.

Magnetic resonance imaging clinics of North America
Artificial intelligence (AI) can provide significant utility in the management of hip disorders by analyzing MR images. AI can automate image segmentation with success. Current models have been successfully tested in the diagnosis of osteoarthritis, ...

Research on the Motion Control Strategy of a Lower-Limb Exoskeleton Rehabilitation Robot Using the Twin Delayed Deep Deterministic Policy Gradient Algorithm.

Sensors (Basel, Switzerland)
The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Determini...

Deep Learning for Automated Classification of Hip Hardware on Radiographs.

Journal of imaging informatics in medicine
PURPOSE: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency among radiology reports.

Artificial Intelligence Models Are Limited in Predicting Clinical Outcomes Following Hip Arthroscopy: A Systematic Review.

JBJS reviews
BACKGROUND: Hip arthroscopy has seen a significant surge in utilization, but complications remain, and optimal functional outcomes are not guaranteed. Artificial intelligence (AI) has emerged as an effective supportive decision-making tool for surgeo...

Artificial intelligence-based assessment of leg axis parameters shows excellent agreement with human raters: A systematic review and meta-analysis.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: The aim of this study was to conduct a systematic review and meta-analysis on the reliability and applicability of artificial intelligence (AI)-based analysis of leg axis parameters. We hypothesized that AI-based leg axis measurements would ...

Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction.

Topics in magnetic resonance imaging : TMRI
OBJECTIVES: The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for t...

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare.

Scientific reports
AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment. Despite the remarkable advancements in AI algorithms for medical imaging, the potential for biases...