Explainable machine learning for orthopedic decision-making: predicting functional outcomes of total hip replacement from gait biomechanics.

ABSTRACT: This study aimed to identify subpopulations of patients with hip osteoarthritis who exhibit distinct adaptations in gait biomechanics, and to evaluate subpopulation-specific effects of total hip replacement on gait biomechanics. Three datasets were analyzed: (1) a cohort of 109 unilateral hip osteoarthritis patients before total hip replacement, (2) a subset of the first dataset of 63 patients re-evaluated after total hip replacement and (3) a control group of 56 healthy individuals. For all participants, three-dimensional joint angle and moment waveforms of the pelvis, ipsilateral hip and knee, as well as sagittal-plane ankle motion and the foot progression angle, were obtained. The analytical framework integrated k-means clustering, support vector machine classifiers, Shapley Additive ex

SUBMITTER: Stetter BJ 

PROVIDER: S-EPMC12743397 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature