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

Objective

Artificial 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).

Design

Baseline 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 receiver operating characteristic curves.

Results

Compared to the population average, the metabolic cluster had a higher prevalence of cartilage lesions, while the post-traumatic one had more medial meniscal damage. Random forest models showed the best prediction (area under the curve 0.837, test set at 2 years). The top predictors for KR were meniscal position (relative to the border of the tibial plateau), severe joint effusion, medial femorotibial cartilage lesions, and metabolic phenotype. These features defined patients at high risk of KR with an estimated KR rate at 5 years of 10% vs 3% in the high- and low-risk groups based on a predictive risk score including all analyzed structures.

Conclusions

This ML-enabled assessment of multiple MRI pathologies in a large KOA data set highlights the importance of meniscal pathologies and markers of inflammation, in addition to cartilage assessments and clinical information for patient stratification and improved prediction of KOA progression to KR.