Machine learning-based prediction of retear after arthroscopic rotator cuff repair: a multicenter cohort study.
Authors
Affiliations (6)
Affiliations (6)
- Department of Orthopaedic Surgery, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
- Department of Orthopaedic Surgery, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea.
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
- Department of Orthopaedic Surgery, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
- School of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea.
- Department of Computer Engineering, Hongik University, Seoul, Republic of Korea.
Abstract
Rotator cuff retears following arthroscopic rotator cuff repair (ARCR) remain a critical clinical challenge, influenced by patient, tear, and rehabilitation factors. Conventional assessment methods struggle to capture this multifactorial complexity, limiting accurate retear prediction and personalized management. The purpose of this study was to develop a machine learning-based predictive model to stratify the risk of structural retear at 6 months post-operatively. This study analyzed 1,428 patients with complete datasets from an initial cohort of 3,090 patients who underwent ARCR between January 2011 and May 2024 across four centers. A total of 79 clinical variables were collected, including patient demographics, tear size, range of motion, muscle strength, and pain and functional scores at baseline and at 2-4 months post-operatively. Retear was defined as Sugaya classification type IV or V on standardized 6-month post-operative magnetic resonance imaging. Five machine learning models (Logistic Regression, k-Nearest Neighbors, Support Vector Machine, Convolutional Neural Network, and Multi-Layer Perceptron; [MLP]) were evaluated for retear prediction. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. The MLP model showed the most favorable numerical performance among the evaluated models, with a retear-class F1-score of 0.76 and precision of 0.92 under a random 80:20 split, and the largest area under the receiver operating characteristic curve among the models tested. However, under temporal validation the retear-class F1-score fell to 0.30, indicating limited generalizability over time. The MLP model demonstrated potential for stratifying the risk of magnetic resonance imaging-defined 6-month structural retear after ARCR and may serve as an adjunctive tool for early post-operative risk stratification. Because it relies on post-operative predictors and has been evaluated only within a single hospital network, external validation is required before clinical use.