Predicting postoperative outcomes in small rotator cuff tears: An AI-based approach to long head of the biceps management - 27/10/26

Doi : 10.1016/j.otsr.2026.104933

Rayane Benhenneda ^a,⁎ , Thierry Brouard ^b, Ramy Samargandi ^c, Christophe Charousset ^d, Julien Berhouet ^a,^b

^a Service de Chirurgie Orthopédique, Hôpital Trousseau, CHRU de Tours, Faculté de Médecine, Université de Tours Centre-Val de Loire, France

^b LIFAT (EA6300), Ecole Polytechnique Universitaire de Tours, 64 avenue Jean Portalis, Tours, 37200, France

^c Orthopedic Surgery Department, Faculty of medicine, University of Jeddah, Jeddah, Saudi Arabia

^d Clinique Turin, Paris, France

^⁎ Corresponding author at: Service de Chirurgie Orthopédique, Hôpital Trousseau, CHRU de Tours, avenue de la République, Chambray-lès-Tours, 37044 Tours cedex 9, France. Faculté de Médecine, université de Tours, France. Service de Chirurgie Orthopédique, Hôpital Trousseau, CHRU de Tours, avenue de la République, Chambray-lès-Tours, 37044 Tours cedex 9, France. Faculté de Médecine, université de Tours France

Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Tuesday 27 October 2026

Abstract

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.