ArticleScientific reports2021
Automation of surgical skill assessment using a three-stage machine learning algorithm.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 65 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
65 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Objective technical skills assessment in laparoscopic cholecystectomy: a systematic review of manual, kinematic, and AI-based tools.Surgical endoscopy · 2026Pooled it
- Artificial intelligence augmented tutoring vs expert instruction on learning simulated general surgical skills: a systematic review and meta-analysis.BMC medical education · 2026Pooled it
- Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review.The British journal of surgery · 2024Pooled it
- Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.Surgical endoscopy · 2023Pooled it
- Video-based tools for surgical quality assessment of technical skills in laparoscopic procedures: a systematic review.Surgical endoscopy · 2023Pooled it
- Proprietary platform artificial intelligence-based segmentation of surgical videos facilitates review and rating of operative recordings.Surgical endoscopy · 2026Article
- Video-based analysis of camera-instrument distance dynamics and operative efficiency in expert and novice robot-assisted partial nephrectomy.Journal of robotic surgery · 2026Article
- See more, detect more: investigating colonic inspection techniques and polyp detection using machine learning.Surgical endoscopy · 2026Article
- An interpretable whole-stream video based deep learning framework for surgical skill assessment of basic procedural elements.International journal of surgery (London, England) · 2026Article
- Artificial intelligence-based approaches to augmenting and automating surgical training.BJU international · 2026Review
- The future of robotic surgery in the age of artificial intelligence.Nature reviews. Urology · 2026Review
- Evaluating and Classifying Gentleness in VR-Based Surgical Simulation: A VR + fNIRS Study.Sensors (Basel, Switzerland) · 2026Article
- An Australian perspective of using video for the assessment of laparoscopic surgery and support for artificial intelligence in performance evaluation.Langenbeck's archives of surgery · 2026Article
- Quantitative Analysis of Instrument Motion Paths in Cataract Surgery across a Resident's Training.Ophthalmology science · 2026Article
- Artificial Intelligence in Minimally Invasive and Robotic Gastrointestinal Surgery: Major Applications and Recent Advances.Journal of personalized medicine · 2026Review
- The interpretable surgical temporal informer: explainable surgical time completion prediction.International journal of computer assisted radiology and surgery · 2026Article
- AI model predicts patient outcomes from surgical gestures and provides insights into explainability.npj digital surgery · 2026Article
- Modeling anesthetic-drug label detection for low-resource operating rooms.BMC medical informatics and decision making · 2025Article
- Explainable machine learning using EMG and accelerometer sensor data quantifies surgical skill and identifies biomarkers of expertise.Scientific reports · 2025Article
- Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review.Journal of medical Internet research · 2025Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Surgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.
Identifiers
What OpenQuestion holds
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.