Summary of Recap: Recursive Cross Attention Network For Pseudo-label Generation in Robotic Surgical Skill Assessment, by Julien Quarez et al.
ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in Robotic Surgical Skill Assessment
by Julien Quarez, Marc Modat, Sebastien Ourselin, Jonathan Shapey, Alejandro Granados
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel recurrent transformer model is proposed to track a surgeon’s performance throughout a surgical trial, regressing Objective Structured Assessments of Technical Skills (OSATS) scores derived from kinematic data using a clinically motivated objective function. The model outperforms state-of-the-art methods in predicting GRS and average OSATS scores, while matching performance with video-based models. Furthermore, the generation of pseudo-labels at the segment level enables quantitative predictions to be translated into qualitative feedback, facilitating automated surgical skill assessment pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Surgical skills are crucial for patient care. To help surgeons improve, two popular tools are used: Objective Structured Assessments of Technical Skills (OSATS) and Global Rating Scale (GRS). These measures provide feedback to aid in training and reaching practice standards. A new model uses kinematic data to track a surgeon’s performance during a surgical trial, predicting OSATS scores and GRS ratings. This innovative approach outperforms other methods and can provide valuable insights for surgeons. |
Keywords
* Artificial intelligence * Objective function * Transformer
