Shoulder Rotator Cuff Tear Detection from Ultrasound Videos Using Deep Reinforcement Learning

Rotator cuff tears (RCT) are common musculoskeletal injuries that significantly impact patient mobility and quality of life. Ultrasound (US) is a real-time, cost-effective tool for diagnosing RCT, offering dynamic assessment through video sweeps. However, these videos are noisy and challenging to interpret, even for experts. They are also often quite long and contain redundant frames which make it more time-consuming for clinicians to identify key diagnostic details. To address this, we developed a novel RCT assessment technique using Deep Reinforcement Learning (DRL)-based video summarization approach, where the RL-agent mimics a human expert by analyzing the full video, selecting diagnostically relevant frames, and then using CNN to classify the frames as intact or torn. The innovative reward mechanism, emphasizing the frame dissimilarity and feature similarity, enhances the RL agent's frame selection process. This reduces computationa complexity and storage requirements and simplifies the downstream classification task by focusing only on the keyframes indicative of RCTs. Experimental results on 100 patients demonstrate that our proposed classification network achieved 85.0% accuracy (90.0% sensitivity, 80.0% specificity) using the RL-generated video summary, outperforming full video classification accuracy of 82.5%, while also reducing the training time by 50% (from 8 hours to 4 hours), highlighting its potential to assist clinicians in more effective diagnosis. The DRL approach developed in this work can be integrated into a low-cost US-AI tool that can reliably identify RCTs, thus improving access to imaging, providing earlier disease diagnosis and enhancing patient care.