Artificial Intelligence Predicts Failure of Nonoperative Treatment in Degenerative Posterosuperior Rotator Cuff Tears

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Purpose: To develop an artificial intelligence‐based algorithm to predict success of nonoperative treatment in degenerative posterosuperior tears and to identify the most influential features. Methods: Patients with degenerative rotator cuff tears initially treated nonoperatively were enrolled prospectively from January 2022 to November 2024. Exclusion criteria included age >75 years; 2); and severe fatty degeneration (Goutallier >2). All patients followed a standardized physiotherapy protocol and could choose surgery if symptoms persisted; failure was defined as opting for surgery. Collected variables included demographics, comorbidities (diabetes, dyslipidemia, hypertension, hypothyroidism, and smoking), magnetic resonance imaging tear characteristics (size; International Society of Arthroscopy, Knee Surgery, and Orthopaedic Sports Medicine classification; associated cuff involvement; and biceps pathology), corticosteroid injection history, and functional scores (American Shoulder and Elbow Surgeons score, Subjective Shoulder Value, and the visual analog scale). First, a standard statistical analysis between groups was performed. Later, the dataset was split into a train (80%) and test (20%), and a random forest algorithm model was developed. Finally, SHapley Additive exPlanations values were computed to assess feature importance. Results: Of the 412 patients enrolled, 347 patients met the inclusion criteria. After 24 months, 66 (19%) patients opted for surgery. Mean follow‐up was 30.4 months (range, 24‐32). Compared with the successful nonoperative treatment group, patients who failed treatment were younger (58.5 vs 64.6 years, P = .001) and had higher body mass index (28.8 vs 27.2, P = .016), greater subscapularis involvement (13.5% vs 3%, P = .042), higher visual analog scale scores (7.7 vs 7.2, P = .018), lower Subjective Shoulder Value (30.9 vs 38.9, P = .003), and lower American Shoulder and Elbow Surgeons scores (40.6 vs 47.1, P = .001). The model achieved an area under the curve of 84%, predicting success rate of the nonoperative treatment with 94% sensitivity, 62% specificity, 89% positive predictive value, and 77% negative predictive value. The most influential variables for the model were age, tear size, and pretreatment functional scores. Conclusions: The artificial intelligence‐based algorithm accurately predicted nonoperative treatment outcomes in degenerative posterosuperior rotator cuff tears (area under the curve 0.84, sensitivity 94%). Age, tear size, and function were the main predictors. Level of Evidence: Level IV, prognostic retrospective case series.

This publication has 63 references indexed in Scilit: