MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries

Objective To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries. Methods According to arthroscopy findings, 3087 patients were divided into normal, degenerative, and tear groups (groups 0–2). Group 2 was further divided into bursal-side, articular-side, intratendinous, and full-thickness tear groups (groups 2.1–2.4), and external validation was performed with 573 patients. Visual geometry group network 16 (VGG16) was used for preliminary image screening. Then, the rotator cuff multitask learning (RC-MTL) model performed multitask classification (classifiers 1–4). A multistage decision model produced the final output. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis and calculation of related parameters. McNemar’s test was used to compare the differences in the diagnostic effects between radiologists and the model. The intraclass correlation coefficient (ICC) was used to assess the radiologists’ reliability. p < 0.05 indicated statistical significance. Results In the in-group dataset, the area under the ROC curve (AUC) of VGG16 was 0.92, and the average AUCs of RC-MTL classifiers 1–4 were 0.99, 0.98, 0.97, and 0.97, respectively. The average AUC of the automated multitask deep learning system for groups 0–2.4 was 0.98 and 0.97 in the in-group and out-group datasets, respectively. The ICCs of the radiologists were 0.97–0.99. The automated multitask deep learning system outperformed the radiologists in classifying groups 0–2.4 in both the in-group and out-group datasets ( p < 0.001). Conclusion The MRI-based automated multitask deep learning system performed well in diagnosing SST injuries and is comparable to experienced radiologists. Clinical relevance statement Our study established an automated multitask deep learning system to evaluate supraspinatus tendon (SST) injuries and further determine the location of SST tears. The model can potentially improve radiologists’ diagnostic efficiency, reduce diagnostic variability, and accurately assess SST injuries. Key Points • A detailed classification of supraspinatus tendon tears can help clinical decision-making. • Deep learning enables the detailed classification of supraspinatus tendon injuries. • The proposed automated multitask deep learning system is comparable to radiologists.