ArticleJournal of surgical education2024
Assessment of Open Surgery Suturing Skill: Image-based Metrics Using Computer Vision.
Article in Journal of surgical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- [Application of artificial intelligence for surgical skill assessment and quality control in minimally invasive surgery: progress, problems and prospect].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- VasSurDetect: An AI-based Chatbot for Rapid, Photo-based Feedback in Microsurgical Suturing Training.Plastic and reconstructive surgery. Global open · 2026Article
- Developing and evaluating image-based metrics for open surgical suturing skill assessment.Global surgical education : journal of the Association for Surgical Education · 2026Article
- Evaluation of Transfer Learning Efficacy for Surgical Suture Quality Classification on Limited Datasets.Journal of imaging · 2025Article
- Artificial intelligence integration in surgery through hand and instrument tracking: a systematic literature review.Frontiers in surgery · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveThis paper presents a computer vision algorithm for extraction of image-based metrics for suturing skill assessment and the corresponding results from an experimental study of resident and attending surgeons.
designA suturing simulator that adapts the radial suturing task from the Fundamentals of Vascular Surgery (FVS) skills assessment is used to collect data. The simulator includes a camera positioned under the suturing membrane, which records needle and thread movement during the suturing task. A computer vision algorithm processes the video data and extracts objective metrics inspired by expert surgeons' recommended best practice, to "follow the curvature of the needle." PARTICIPANTS AND
resultsExperimental data from a study involving subjects with various levels of suturing expertise (attending surgeons and surgery residents) are presented. Analysis shows that attendings and residents had statistically different performance on 6 of 9 image-based metrics, including the four new metrics introduced in this paper: Needle Tip Path Length, Needle Swept Area, Needle Tip Area and Needle Sway Length. CONCLUSION AND SIGNIFICANCE: These image-based process metrics may be represented graphically in a manner conducive to training. The results demonstrate the potential of image-based metrics for assessment and training of suturing skill in open surgery.
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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.