Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI

Abstract

This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of the models were evaluated and compared using receiver operating curves and the area under the curve (AUC). The AUC was the highest when using all imaging planes (0.94; p < 0.05). Among a single imaging plane, the axial plane showed the best performance (AUC: 0.71), followed by the sagittal (AUC: 0.70) and coronal (AUC: 0.68) imaging planes. The sensitivity and accuracy were also the highest in the model with all-plane training (0.98 and 0.96, respectively). Thus, deep-learning-based automatic rotator cuff tear detection can be useful for detecting torn areas in various regions of the rotator cuff in all three imaging planes.

Keywords: deep learning; magnetic resonance imaging; rotator cuff tear.

Figure 1

Flowchart of the study (RCT: rotator cuff tear).

Figure 2

Segmentation of torn rotator cuff tendons on all three imaging planes. The segmentation is performed by drawing freeform lines (red) outlining all rotator cuff tears, including the supraspinatus, infraspinatus, and subscapularis, within all three imaging planes. Multiple areas of rotator cuff tears were segmented separately.

Figure 3

Network architecture of prediction model for rotator cuff tear.

Figure 4

ROC curves for the model using all imaging planes (red) and using only axial (blue), sagittal (green), and coronal (black) images.