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

Stetter, Bernd J. ORCID iD icon 1; Dully, Jonas 1; Stief, Felix; Holder, Jana; Steingrebe, Hannah ORCID iD icon 1; Zaucke, Frank; Sell, Stefan 1; Drongelen, Stefan van; Stein, Thorsten 1
1 Institut für Sport und Sportwissenschaft (IfSS), Karlsruher Institut für Technologie (KIT)

Abstract:

This study aimed to identify subpopulations of patients with hip steoarthritis 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
exPlanations, and statistical waveform analyses. Clustering of the pre-operative dataset revealed three distinct subpopulations characterized by unique patterns in gait kinematics and joint moments. These subpopulations also differed in age, Kellgren-Lawrence score, and walking speed. Prior to total hip replacement, between 51.4% and
85.2% of hip osteoarthritis patients were classified as pathologic; following surgery, this proportion decreased to 27.8% − 51.8%. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000189304
Veröffentlicht am 07.01.2026
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Sport und Sportwissenschaft (IfSS)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 27.12.2025
Sprache Englisch
Identifikator ISSN: 1478-6362
KITopen-ID: 1000189304
Erschienen in Arthritis Research & Therapy
Verlag Springer Fachmedien Wiesbaden
Band 27
Heft 1
Seiten Art.-Nr.: 229
Vorab online veröffentlicht am 23.12.2025
Nachgewiesen in Scopus
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