Multicenter Study of YOLOv9 for Automated Detection and Classification of Supraspinatus Tendon Tears on MRI.

RATIONALE AND

OBJECTIVES

This study develops a deep learning model using the You Only Look Once (YOLO) framework for the automated diagnosis of supraspinatus tendon tears (ST) based on multicenter MRI data.

MATERIALS AND

METHODS

In this retrospective study, 1698 patients from five hospitals were included and allocated to training (n=1047), validation (n=299), test (n=154), and external test (n=198) sets. A YOLOv9-based automated model was developed using coronal fat-suppressed T2-weighted images for lesion detection, localization, and classification. Model performance was assessed using Intersection over Union and confusion matrices. Comparisons between model outputs and radiologist interpretations were performed with McNemar's test, and interobserver agreement among radiologists was evaluated using Cohen's kappa.

RESULTS

The YOLOv9 model successfully identified the supraspinatus tendon layer in all images across the validation, test, and external test sets, achieving 100% accuracy. For ST tear detection, the model achieved accuracies of 69.0% (755/1094) in the validation set, 73.9% (414/560) in the test set, and 75.64% (559/739) in the external test set. For classification of partial- and full-thickness tears on the test set, the model demonstrated a macro F1 score of 77.7% (95% CI: 67.4-90.5), outperforming all radiologists (all P<0.05).

CONCLUSION

The MRI-based YOLOv9 model excelled in diagnosing supraspinatus tendon tears, surpassing radiologists with varying levels of experience.