基于人工智能预测内侧半月板后根撕裂非手术治疗后的预后
简介
回顾性纳入233例内侧半月板后根撕裂（MMPRT）非手术治疗患者，开发并验证基于人工智能的预测模型，预测2年和5年临床失败（转为TKA或截骨术）风险。深度学习GGAT模型在2年和5年随访中预测性能均优于传统机器学习模型，AUC为0.69~0.77；基线机械轴（HKA角）、症状持续时间、BMI、年龄等为最重要预测因素。该模型或可辅助临床决策与患者咨询，但证据等…
英文摘要
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.