ArticlePlastic and reconstructive surgery. Global open2026
VasSurDetect: An AI-based Chatbot for Rapid, Photo-based Feedback in Microsurgical Suturing Training.
Article in Plastic and reconstructive surgery. Global open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
7 authors.
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Abstract
Background: Microsurgical suturing training requires precise, repeated feedback, but faculty feedback is often delayed, subjective, and limited by availability. We developed VasSurDetect, a LINE mobile messaging application (LY Corporation, Tokyo, Japan)-based artificial intelligence chatbot, to provide rapid photo-based feedback on vascular suturing end-product quality. Methods: A total of 350 paired microscope images from simulation-based chicken wing vessel practice were collected as inside and outside views. Knots, purchase segments, and leftover suture material were annotated. Nine object detection models were trained and compared. Bounding-box outputs were converted into quantitative metrics for stitch number, purchase length, interstitch distance, stitch angle, stitch axis, and leftover suture length. The selected model was deployed in a chatbot workflow that accepts 2 microscope photographs and returns an automated report. Results: For inside-view detection, YOLOv10 achieved the highest overall performance, with an F1 score of approximately 0.89. For outside-view detection, YOLOv8 and YOLOv10 performed comparably, each achieving an F1 score of approximately 0.55. YOLOv10 was selected for system deployment. The implemented workflow generated stitch-level measurements, including stitch spacing, angular alignment, and leftover suture length, with a mean spacing error of approximately 0.4 mm and an angular alignment error of approximately 10 degrees. Automated feedback was delivered within 10 seconds. Conclusions: VasSurDetect provides rapid, standardized, image-based feedback for microsurgical suturing practice without additional hardware. It is intended to complement, not replace, expert supervision by supporting high-frequency deliberate practice and objective performance tracking in simulation-based microsurgical training.
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Registered trials
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