ArticleScientific reports2023
Video-based formative and summative assessment of surgical tasks using deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training.Scientific reports · 2024Trial
- Artificial intelligence-supported segmentation of cardiac anatomy in open-heart surgery videos.Journal of robotic surgery · 2026Article
- [Research on surgical instrument segmentation algorithm based on frequency-domain adaptive feature decomposition and visual Mamba].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review.Journal of medical Internet research · 2025Article
- Deep learning for video-based assessment of endotracheal intubation skills.Communications medicine · 2025Article
- Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication.Global surgical education : journal of the Association for Surgical Education · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated-none of which is currently achievable. Video-based assessment (VBA) is being deployed in intraoperative and simulation settings to evaluate technical skill execution. However, VBA is manual, time-intensive, and prone to subjective interpretation and poor inter-rater reliability. Herein, we propose a deep learning (DL) model that can automatically and objectively provide a high-stakes summative assessment of surgical skill execution based on video feeds and low-stakes formative assessment to guide surgical skill acquisition. Formative assessment is generated using heatmaps of visual features that correlate with surgical performance. Hence, the DL model paves the way for the quantitative and reproducible evaluation of surgical tasks from videos with the potential for broad dissemination in surgical training, certification, and credentialing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.