Age and medial compartmental OA were important predictors of the lateral compartmental OA in the discoid lateral meniscus: Analysis using machine learning approach

Abstract Purpose The objective of this study was to develop a machine learning model that would predict lateral compartment osteoarthritis (OA) in the discoid lateral meniscus (DLM), from which to then identify factors contributing to lateral compartment OA, with a key focus on the patient's age. Methods Data were collected from 611 patients with symptomatic DLM diagnosed using magnetic resonance imaging between April 2003 and May 2022. Twenty features, including demographic, clinical and radiological data and six algorithms were used to develop the predictive machine learning models. Shapley additive explanation (SHAP) analysis was performed on the best model, in addition to subgroup analyses according to age. Results Extreme gradient boosting classifier was identified as the best prediction model, with an area under the receiver operating characteristic curve (AUROC) of 0.968, the highest among all the models, regardless of age (AUROC of 0.977 in young age and AUROC of 0.937 in old age). In the SHAP analysis, the most predictive feature was age, followed by the presence of medial compartment OA. In the subgroup analysis, the most predictive feature was age in young age, whereas the most predictive feature was the presence of medial compartment OA in old age. Conclusion The machine learning model developed in this study showed a high predictive performance with regard to predicting lateral compartment OA of the DLM. Age was identified as the most important factor, followed by medial compartment OA. In subgroup analysis, medial compartmental OA was found to be the most important factor in the older age group, whereas age remained the most important factor in the younger age group. These findings provide insights that may prove useful for the establishment of strategies for the treatment of patients with symptomatic DLM. Level of Evidence Level III.