Diagnostic Accuracy and Interobserver Reliability of Rotator Cuff Tear Detection With Ultrasonography Are Improved With Attentional Deep Learning

PURPOSE: To improve the accuracy of 1-stage object detection by modifying the YOLOv7 with the convolutional block attention module (CBAM), known as YOLOv7-CBAM, which can automatically identify torn or intact rotator cuff tendon to assist physicians in diagnosing rotator cuff lesions through ultrasound. METHODS: Between 2020 and 2021, patients who experienced shoulder pain for over 3 months and had both ultrasound and magnetic resonance imaging examinations were categorized into torn and intact groups. To ensure balanced training, we included the same number of patients in both groups. Transfer learning was conducted using a pretrained model of YOLOv7 and an improved model with CBAM. The mean average precision, sensitivity, and F1-score were calculated to evaluate the models. A gradient-weighted class activation mapping method was employed to visualize important regions using a heatmap. A simulation data set was recruited to evaluate the diagnostic performance of clinical physicians using our artificial intelligence-assisted model. RESULTS: A total of 280 patients were included in this study, with 80% of 840 ultrasound images randomly allocated for model training. The accuracy for the test set was 0.96 for YOLOv7 and 0.98 for YOLOv7-CBAM, and the precision and sensitivity were 0.94 and 0.98 for YOLOv7 and 0.98 and 0.98 for YOLOv7-CBAM. F1-score and mean average precision were higher for YOLOv7-CBAM (0.980 and 0.993) than YOLOv7 (0.961 and 0.965). Furthermore, the gradient-weighted class activation mapping method elucidated that the deep learning model primarily emphasized a hypoechoic anechoic defect within the tendon. Following adopting an artificial intelligence-assisted model (YOLOv7-CBAM model), diagnostic accuracy improved from 80.86% to 88.86% (P = .01), and interobserver reliability improved from 0.49 to 0.71 among physicians. CONCLUSIONS: The YOLOv7-CBAM model shows high accuracy in detecting torn or intact rotator cuff tendon from ultrasound images. Integrating this model into the diagnostic process can assist physicians in improving diagnostic accuracy and interobserver reliability across different physicians. CLINICAL RELEVANCE: The attentional deep learning model aids physicians in improving the accuracy and consistency of ultrasound diagnosis of torn or intact rotator cuff tendons.