Detection and classification of supraspinatus pathologies on shoulder magnetic resonance images using a code-free deep learning application.

Objective: To evaluate the performance of a code free deep learning (CFDL) application in diagnosing supraspinatus tendon pathologies on shoulder magnetic resonance imaging (MRI) images. Design: This retrospective cross-sectional study included patients with supraspinatus MRI showing partial or full-thickness tears and tendinosis, with patients having normal findings as the control group. MRI images were processed in the LobeAI application using transfer learning with ResNet-50 V2 for model development. Models were built to differentiate each pathology from normal and full-thickness tears from partial tears. Results: The ML models developed using the LobeAI application demonstrated the ability to differentiate between normal shoulder MRI images and partial tears, full-thickness tears, and tendinosis with sensitivities of 93.75 %, 100 %, and 100 %, respectively, and specificities of 43.75 %, 62.5 %, and 18.75 %. The model designed to classify partial vs. full-thickness tears achieved an accuracy of 34.38 %. The model incorporating all pathological images compared to normal MRI images exhibited an accuracy of 37.50 % and a weighted F1 score of 0.32. Conclusion: The results of the study suggest that, although CFDL applications may be promising for the initial detection of supraspinatus pathologies, their current iteration has limitations that must be resolved before they can be reliably integrated into clinical practice.