Ultrasound for Automated Classification of Full-Thickness Rotator Cuff Tendon Tears using Deep Learning

Rotator cuff tendon tears, the most common shoulder injuries, are typically diagnosed mainly through MRI, but can also be seen on ultrasound (US), a much less costly test that currently requires highly-trained human expert operators. An AI tool to identify full-thickness rotator cuff tears in US could make this test much more accessible in clinical practice. We propose a two-step approach starting with segmentation and followed by classification. Automatic segmentation of US scans is challenging due to speckle noise and low contrast. We utilized a CNN-autoencoder that predicts boundary contour points of humeral cortex and subacromial bursa directly from raw US images rather than the popular pixel-wise semantic segmentation. Then both the original US image and the corresponding segmentation mask are passed to a classification network (VGG-16) to determine whether tendons are torn or intact. This novel approach only passes the key portions of the scan (in which any tears are most visible) to the classification network, maximizing detection accuracy and clinical relevance. We evaluated this approach on data prospectively acquired from 210 patients, training with 11,600 images and testing with 2900 images. We had an average segmentation Dice coefficient (DC) of 95.3% and Hausdorff Distance (HD) of 2.9 mm, outperforming a U-Net model (DC=90.5%, HD=6.8 mm). The classification network, VGG-16, achieved 85.2% accuracy (sensitivity 84.2%, specificity 83.3%) in classifying supraspinatus tendons as intact or torn from US images. Results indicate that our AI-driven US evaluation pipeline has the potential to enable less-experienced ultrasound users to detect rotator cuff tears with high accuracy and explainability. This can allow more healthcare professionals to conduct scans, improving timely patient access to imaging and streamlining treatment decisions.