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Knee Injuries

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On-field player workload exposure and knee injury risk monitoring via deep learning.

Journal of biomechanics
In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload exposure and knee injury risk. Traditionally, this analysis has relied on captive laboratory force plate...

Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection.

Radiology
Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) wit...

Assessment of knee laxity using a robotic testing device: a comparison to the manual clinical knee examination.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: The purpose of this study was to collect knee laxity data using a robotic testing device. The data collected were then compared to the results obtained from manual clinical examination.