Shoulder Rotator Cuff Tear Detection from Ultrasound Videos Using Deep Reinforcement Learning
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".