Arthroscopy, sports medicine, and rehabilitation
Artificial Intelligence Predicts Failure of Nonoperative Treatment in Degenerative Posterosuperior Rotator Cuff Tears
Par Gabardo S, Martínez-Catalán N, Belmonte Martínez JM et 6 autres.
Contenu en anglais
Résumé
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; <24 months of follow-up; massive, traumatic, and partial tears; arthritic changes (Hamada >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.