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Background: Knee Osteoarthritis (KOA) is a prevalent condition worldwide, significantly diminishing quality of life and productivity. Except for the alignment change, muscle activation patterns (MAP) have garnered increasing attention as another crucial factor contributing to KOA.

Objective: This study explores the factors, characteristics, and effects of MAP changes caused by KOA, providing a neuromuscular-based causal analysis for the rehabilitation treatment of KOA.

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Background: The intercondylar space is defined as the topographic area walled by the intercondylar notch (IN) and encasing the central ligaments of the knee joint. As the anterior cruciate ligament (ACL) is not only impinged against the roof but also against the lateral wall of the IN, information concerning changes to the IN during the progression of osteoarthritis could be potentially relevant in predicting the future risk for ACL-rupture and -degeneration and ensure preventive measures as early as possible. Therefore, the purpose of this study was to evaluate the influence of osteoarthritis on osseous notch morphology.

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Previous evidence highlights the important role of knee joint malalignment and excessive joint moments for the development to knee osteoarthritis. The present study aimed to systematically investigate the interrelationship between three-dimensional knee kinematics during walking and stair climbing and ex-vivo electromechanical measured cartilage quality in 119 patients with end-stage knee osteoarthritis. Patients scheduled for total knee arthroplasty surgery underwent radiographic assessment and biomechanical analysis in gait and stair climbing assessing in vivo knee joint angles and moments during movement dynamics prior to surgery.

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A Deep Learning Tool for Minimum Joint Space Width Calculation on Anteroposterior Knee Radiographs.

J Arthroplasty

January 2025

Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA; Mayo Clinic Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA. Electronic address:

Background: Minimum joint space width (mJSW) is an important continuous quantitative metric of osteoarthritis progression in the knee. The purpose of this study was to develop an automated measurement algorithm for mJSW in the medial and lateral compartments of the knee that can flexibly handle native knees as well as knees after arthroplasty.

Methods: We developed an end-to-end algorithm consisting of a deep learning segmentation model plus a computer vision algorithm to measure mJSW in the medial and lateral compartments of the knee.

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Purpose: To investigate the failure rate, predictive factors associated with failure and clinical outcomes after a two-stage surgery; meniscus repair followed by subsequent anterior cruciate ligament (ACL) reconstruction (ACLR).

Methods: Patients with a concomitant traumatic meniscus tear and ACL injury who underwent a two-stage surgery between January 2015 and January 2021 were identified. The primary outcome was meniscal repair failure, defined as a reoperation (re-repair or resection).

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