Artificial Intelligence–Based Prediction of Prognosis After Nonsurgical Treatment for Medial Meniscus Posterior Root Tear

Vollständiger Abstract

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Background: Medial meniscus posterior root tear (MMPRT) is recognized as one of the leading causes of knee osteoarthritis. Given the detrimental effects of MMPRT on knee kinematics and the associated clinical consequences, substantial efforts have been directed toward improving the understanding and management of MMPRT. Purpose: To develop and validate an artificial intelligence (AI)–based prediction model for patient-specific risk assessment of clinical failure at 2 and 5 years after nonsurgical treatment of MMPRT. Study Design: Case-control study; Level of evidence, 3. Methods: The authors retrospectively reviewed a prospectively collected database of 233 patients who underwent nonsurgical treatment for MMPRT between 2006 and 2020. Patient descriptive characteristics, clinical data, and imaging variables were evaluated for their association with clinical failure, defined as conversion to total knee arthroplasty or corrective osteotomy at 2- and 5-year follow-up. Five conventional machine learning models, including Elastic Net logistic regression, multilayer perceptron, support vector machine, random forest, and Extreme Gradient Boosting, as well as a proposed deep learning model, the Grouped Graph Attention (GGAT) network, were developed and internally validated to predict clinical failure. Results: During follow-up, clinical failure occurred in 36 of 233 patients (15.5%) at 2 years and in 54 of 233 patients (23.2%) at 5 years. The deep learning–based GGAT model demonstrated better overall predictive performance compared with conventional machine learning models at both the 2- and 5-year follow-up, with test set accuracy of 0.83 to 0.91, precision of 0.87 to 0.91, sensitivity of 0.92 to 1.00, F1 score of 0.89 to 0.95, Brier score of 0.09 to 0.16, and areas under the receiver operating characteristic curve of 0.69 to 0.77. The most influential predictors of clinical failure included baseline mechanical hip-knee-ankle angle, symptom duration, body mass index, age, bone marrow edema, lateral distal femoral angle, subchondral insufficiency fracture of the knee, cartilage lesion, effusion grade, and medial proximal tibial angle. Conclusion: The deep learning–based GGAT network demonstrated accurate prediction of clinical failure at both 2 and 5 years after nonsurgical treatment of MMPRT. These findings underscore the potential value of deep learning–based risk assessment in supporting clinical decision-making and patient counseling.