Combining Machine-Learning Assessment of Multiple MRI Pathologies and Clinical Phenotypes for Predicting Joint Replacement in Knee Osteoarthritis: Data From the Osteoarthritis Initiative.

ABSTRACT: ObjectiveArtificial intelligence offers opportunities for timesaving assessments of multiple pathologies in large magnetic resonance imaging (MRI) data sets in knee osteoarthritis (KOA). This study evaluated their prevalence within pre-defined clinical phenotypes and their predictive value for knee replacement (KR).DesignBaseline MRIs (n = 8,667) from the Osteoarthritis Initiative were analyzed using a machine-learning (ML) algorithm. The presence of pathologies (menisci, anterior cruciate, medial collateral ligaments, cartilage, etc.) was assessed in previously identified phenotypic clusters (a post-traumatic, metabolic, and age-defined phenotype). The value of both, cluster allocation and joint pathology for KR prediction was evaluated using supervised ML models and time-dependent

SUBMITTER: D'Assignies G 

PROVIDER: S-EPMC12681341 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature