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#5970. Towards accurate and interpretable surgical skill assessment: a video-based method for skill score prediction and guiding feedback generation
| August 2026 | publication date |
| Proposal available till | 07-06-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Surgery; Computer Graphics and Computer-Aided Design; Radiology, Nuclear Medicine and Imaging; Health Informatics; Computer Science Applications; Computer Vision and Pattern Recognition; Biomedical Engineering; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №5970.1   | Contract №5970.2 | Contract №5970.3 | Contract №5970.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Purpose: Recently, automatic surgical skill assessment has received the attention given the increasingly important role of surgical training. The assessment usually involves skill score prediction and further feedback generation. Existing work on skill score prediction is limited with several challenges and deserves more promising outcomes. For the feedback, most work identifies the flaws on the granularity of video frames or clips. It thus remains to be explored how to identify poorly performed gestures (segments) and further how to provide good references for improvement. Methods: To overcome these problems, a novel method consisting of three correlated frameworks is proposed. The first framework learns to predict final skill scores of surgical trials with two auxiliary tasks. The second framework learns to predict running intermediate skill scores that indicate the problematic gestures, while the third framework explores the optimal gesture sequences as references through a new Policy Gradient based formulation.
Keywords:
Incorporating recognized surgical gestures and skill levels; Interpretable feedback; Optimal gesture sequences; Surgical skill assessment
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Purpose: Recently, automatic surgical skill assessment has received the attention given the increasingly important role of surgical training. The assessment usually involves skill score prediction and further feedback generation. Existing work on skill score prediction is limited with several challenges and deserves more promising outcomes. For the feedback, most work identifies the flaws on the granularity of video frames or clips. It thus remains to be explored how to identify poorly performed gestures (segments) and further how to provide good references for improvement. Methods: To overcome these problems, a novel method consisting of three correlated frameworks is proposed. The first framework learns to predict final skill scores of surgical trials with two auxiliary tasks. The second framework learns to predict running intermediate skill scores that indicate the problematic gestures, while the third framework explores the optimal gesture sequences as references through a new Policy Gradient based formulation.
Keywords:
Incorporating recognized surgical gestures and skill levels; Interpretable feedback; Optimal gesture sequences; Surgical skill assessment
Contacts :
