ArticleJSLS : Journal of the Society of Laparoendoscopic Surgeons
Surgical Performance Analysis and Classification Based on Video Annotation of Laparoscopic Tasks.
Article in JSLS : Journal of the Society of Laparoendoscopic Surgeons. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Randomized cross-over trial comparing stress responses amongst undergraduates and surgeons with and without background music during simulated surgery.Scientific reports · 2025Trial
- Artificial intelligence-supported segmentation of cardiac anatomy in open-heart surgery videos.Journal of robotic surgery · 2026Article
- Endoscopic sleeve gastroplasty video annotation: analysis of the learning curve of three annotators to achieve procedure proficiency.International journal of surgery (London, England) · 2026Article
- End to end AI system for surgical gesture sequence recognition and clinical outcome prediction.NPJ digital medicine · 2026Article
- Impact of artificial intelligence on the training of general surgeons of the future: a scoping review of the advances and challenges.Acta cirurgica brasileira · 2024Article
- Automatic Assessment of Procedural Skills Based on the Surgical Workflow Analysis Derived from Speech and Video.Bioengineering (Basel, Switzerland) · 2022Article
- Surgical Gesture Recognition in Laparoscopic Tasks Based on the Transformer Network and Self-Supervised Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Machine learning for technical skill assessment in surgery: a systematic review.NPJ digital medicine · 2022Review
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesCurrent approaches in surgical skills assessment employ virtual reality simulators, motion sensors, and task-specific checklists. Although accurate, these methods may be complex in the interpretation of the generated measures of performance. The aim of this study is to propose an alternative methodology for skills assessment and classification, based on video annotation of laparoscopic tasks.
methodsTwo groups of 32 trainees (students and residents) performed two laparoscopic tasks: peg transfer (PT) and knot tying (KT). Each task was annotated via a video analysis software based on a vocabulary of eight surgical gestures (surgemes) that denote the elementary gestures required to perform a task. The extracted metrics included duration/counts of each surgeme, penalty events, and counts of sequential surgemes (transitions). Our analysis focused on trainees' skill level comparison and classification using a nearest neighbor approach. The classification was assessed via accuracy, sensitivity, and specificity.
resultsFor PT, almost all metrics showed significant performance difference between the two groups (
conclusionThe proposed technique provides a tool for skills assessment and experience classification of surgical trainees, as well as an intuitive way for describing what and how surgemes are performed.
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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.