Predicting Postoperative Outcomes in Small Rotator Cuff Tears: An AI-Based Approach to Long Head of the Biceps Management.

INTRODUCTION: The management of the long head of the biceps (LHB) tendon in small rotator cuff tears remains controversial, particularly when the tendon appears macroscopically healthy. While prophylactic tenotomy or tenodesis is often recommended for pathological LHB, the decision-making process for an apparently normal LHB remains unclear. This study aimed to develop and evaluate an AI-based model for predicting postoperative outcomes in patients with small rotator cuff tears and a seemingly healthy LHB, assisting in the decision between preservation and tenotomy. METHODS: This was a prospective, multicenter, single-blind study including 199 patients with small rotator cuff tears. Preoperative clinical, imaging, and arthroscopic data were collected. A deep learning model, integrating a convolutional neural network (CNN) for arthroscopic image analysis and a multilayer perceptron (MLP) for clinical and imaging data, was developed to predict the Constant score at 24 months follow-up. The model's predictions were compared with traditional statistical analyses, and subgroup analyses were performed on patients with a preserved healthy LHB and those with a poor functional outcome, defined by a Constant score <70 at follow-up. RESULTS: The AI model accurately predicted the Constant score at 24 months in 91.4% of cases, with a mean prediction error of 6.04 points. In the "preserved healthy LHB" subgroup, the model achieved 93% accuracy, with a mean deviation of 5.45 points. However, in the "failed healthy LHB preserved" subgroup, accuracy dropped to 59.2%, with a mean deviation of 18 points. The AI model overestimated outcomes in 8 cases (leading to incorrect preservation of the LHB) and underestimated in 6 cases. CONCLUSION: An AI model combining arthroscopic imaging and clinical data can predict postoperative Constant scores with high accuracy. However, its performance is limited in predicting failures, particularly in patients with a preserved LHB who later develop poor outcomes. While AI may complement conventional statistical methods in clinical decision-making, further validation with larger datasets and refined variables is necessary to enhance its reliability and clinical relevance. LEVEL OF EVIDENCE: III; Diagnostic Study.