ArticleAnnals of surgery open : perspectives of surgical history, education, and clinical approaches2023
Developing surgical skill level classification model using visual metrics and a gradient boosting algorithm.
Article in Annals of surgery open : perspectives of surgical history, education, and clinical approaches, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.Surgical endoscopy · 2023Pooled it
- Assessment of perceived objectivity of clinical evaluation methods in surgical education: an approach using a digital platform.Future science OA · 2026Article
- Artificial intelligence in robotic urologic surgery: a scoping review.World journal of urology · 2026Article
- Balancing Objectivity and Humanity: Ethical Challenges and Considerations in Surgical Candidacy Decisions.HEC forum : an interdisciplinary journal on hospitals' ethical and legal issues · 2026Article
- Evaluating and Classifying Gentleness in VR-Based Surgical Simulation: A VR + fNIRS Study.Sensors (Basel, Switzerland) · 2026Article
- Multimodal deep learning for objective skill assessment in robot-assisted vesico-urethral anastomosis.Journal of robotic surgery · 2026Article
- Spatiotemporal characteristics of eye-hand coordination among different skill levels in laparoscopic surgery.Surgical endoscopy · 2026Article
- Gaze Characteristics Using a Three-Dimensional Heads-Up Display During Cataract Surgery.Journal of eye movement research · 2025Article
- An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload Evaluation in Surgery.Human factors · 2025Article
- Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning.Surgical endoscopy · 2024Article
- Prediction of Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis using electroencephalography, eye-tracking, and machine learning.Scientific reports · 2024Article
- Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking.NPJ science of learning · 2024Article
- Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.Journal of robotic surgery · 2023Article
- Performance and learning rate prediction models development in FLS and RAS surgical tasks using electroencephalogram and eye gaze data and machine learning.Surgical endoscopy · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Objective: Assessment of surgical skills is crucial for improving training standards and ensuring the quality of primary care. This study aimed to develop a gradient boosting classification model (GBM) to classify surgical expertise into inexperienced, competent, and experienced levels in robot-assisted surgery (RAS) using visual metrics. Methods: Eye gaze data were recorded from 11 participants performing four subtasks; blunt dissection, retraction, cold dissection, and hot dissection using live pigs and the da Vinci robot. Eye gaze data were used to extract the visual metrics. One expert RAS surgeon evaluated each participant's performance and expertise level using the modified Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. The extracted visual metrics were used to classify surgical skill levels and to evaluate individual GEARS metrics. Analysis of Variance (ANOVA) was used to test the differences for each feature across skill levels. Results: Classification accuracies for blunt dissection, retraction, cold dissection, and burn dissection were 95%, 96%, 96%, and 96%, respectively. The time to complete only the retraction was significantly different among the 3 skill levels (p-value = 0.04). Performance was significantly different for 3 categories of surgical skill level for all subtasks (p-values<0.01). The extracted visual metrics were strongly associated with GEARS metrics (R Conclusions: Machine learning (ML) algorithms trained by visual metrics of RAS surgeons can classify surgical skill levels and evaluate GEARS measures. The time to complete a surgical subtask may not be considered a stand-alone factor for skill level assessment.
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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.