Spatial-temporal attention for video-based assessment of intraoperative surgical skill
Abstract Accurate, unbiased, and reproducible assessment of skill is a vital resource for surgeons throughout their career. The objective in this research is to develop and validate algorithms for video-based assessment of intraoperative surgical skill. Algorithms to classify surgical video into exp...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-77176-1 |
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| _version_ | 1850061774498299904 |
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| author | Bohua Wan Michael Peven Gregory Hager Shameema Sikder S. Swaroop Vedula |
| author_facet | Bohua Wan Michael Peven Gregory Hager Shameema Sikder S. Swaroop Vedula |
| author_sort | Bohua Wan |
| collection | DOAJ |
| description | Abstract Accurate, unbiased, and reproducible assessment of skill is a vital resource for surgeons throughout their career. The objective in this research is to develop and validate algorithms for video-based assessment of intraoperative surgical skill. Algorithms to classify surgical video into expert or novice categories provide a summative assessment of skill, which is useful for evaluating surgeons at discrete time points in their training or certification of surgeons. Using a spatial-temporal neural network architecture, we tested the hypothesis that explicit supervision of spatial attention supervised by instrument tip locations improves the algorithm’s generalizability to unseen dataset. The best performing model had an area under the receiver operating characteristic curve (AUC) of 0.88. Augmenting the network with supervision of spatial attention improved specificity of its predictions (with small changes in sensitivity and AUC) and led to improved measures of discrimination when tested with unseen dataset. Our findings show that explicit supervision of attention learned from images using instrument tip locations can improve performance of algorithms for objective video-based assessment of surgical skill. |
| format | Article |
| id | doaj-art-c1bb6a79f1b74b5c8b3e8487449cc31f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c1bb6a79f1b74b5c8b3e8487449cc31f2025-08-20T02:50:07ZengNature PortfolioScientific Reports2045-23222024-11-0114111410.1038/s41598-024-77176-1Spatial-temporal attention for video-based assessment of intraoperative surgical skillBohua Wan0Michael Peven1Gregory Hager2Shameema Sikder3S. Swaroop Vedula4Department of Computer Science, Whiting School of Engineering, Johns Hopkins UniversityDepartment of Computer Science, Whiting School of Engineering, Johns Hopkins UniversityDepartment of Computer Science, Whiting School of Engineering, Johns Hopkins UniversityMalone Center for Engineering in Healthcare, Johns Hopkins UniversityMalone Center for Engineering in Healthcare, Johns Hopkins UniversityAbstract Accurate, unbiased, and reproducible assessment of skill is a vital resource for surgeons throughout their career. The objective in this research is to develop and validate algorithms for video-based assessment of intraoperative surgical skill. Algorithms to classify surgical video into expert or novice categories provide a summative assessment of skill, which is useful for evaluating surgeons at discrete time points in their training or certification of surgeons. Using a spatial-temporal neural network architecture, we tested the hypothesis that explicit supervision of spatial attention supervised by instrument tip locations improves the algorithm’s generalizability to unseen dataset. The best performing model had an area under the receiver operating characteristic curve (AUC) of 0.88. Augmenting the network with supervision of spatial attention improved specificity of its predictions (with small changes in sensitivity and AUC) and led to improved measures of discrimination when tested with unseen dataset. Our findings show that explicit supervision of attention learned from images using instrument tip locations can improve performance of algorithms for objective video-based assessment of surgical skill.https://doi.org/10.1038/s41598-024-77176-1 |
| spellingShingle | Bohua Wan Michael Peven Gregory Hager Shameema Sikder S. Swaroop Vedula Spatial-temporal attention for video-based assessment of intraoperative surgical skill Scientific Reports |
| title | Spatial-temporal attention for video-based assessment of intraoperative surgical skill |
| title_full | Spatial-temporal attention for video-based assessment of intraoperative surgical skill |
| title_fullStr | Spatial-temporal attention for video-based assessment of intraoperative surgical skill |
| title_full_unstemmed | Spatial-temporal attention for video-based assessment of intraoperative surgical skill |
| title_short | Spatial-temporal attention for video-based assessment of intraoperative surgical skill |
| title_sort | spatial temporal attention for video based assessment of intraoperative surgical skill |
| url | https://doi.org/10.1038/s41598-024-77176-1 |
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