Related Experiment Video
Updated: Jan 16, 2026
08:21
Utilizing a 3D Printed Laparoscopic Nissen Fundoplication Model to Shorten a Resident's Learning Curve
Published on: August 15, 2025
592
Automated performance assessment in simulated laparoscopic crural repair
Shekhar Madhav Khairnar1, Bryanna D Stukes1, Sofia Garces Palacios1
1Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
Surgical Endoscopy
|September 29, 2025
Summary
An AI system accurately assessed surgical skill in simulated crural repair by analyzing tool motion. This automated feedback system shows promise for improving laparoscopic surgery training and performance.
Area of Science:
- Surgical simulation and artificial intelligence
- Laparoscopic surgery training and assessment
Background:
- Crural repair reconstructs the esophageal hiatus.
- Objective feedback via AI can enhance surgical trainee performance.
- This study evaluates an AI system for assessing simulated laparoscopic crural repair.
Purpose of the Study:
- To evaluate an AI system for automated assessment of intracorporeal suturing in simulated laparoscopic crural repair.
- To determine if AI can provide objective feedback on trainee performance without manual annotations.
Main Methods:
- 47 videos of 33 participants (15 novices, 18 experts) performing simulated crural repair were analyzed.
- Tool motion was tracked, and kinematic features (path length, RMS velocity, jerk, bimanual dexterity) were extracted.
- Machine learning models were trained and validated using tenfold cross-validation.
Main Results:
- The AI system achieved 74% accuracy and an F1 score of 0.76 using logistic regression.
- Significant differences in bimanual dexterity, tool RMS velocity, RMS jerk, and path length were found between novice and expert groups.
- The AI system reliably differentiated skill levels without manual annotation.
Conclusions:
- The developed AI system reliably assesses surgical skill level in simulated laparoscopic crural repair.
- This automated assessment approach demonstrates potential for widespread application in various laparoscopic procedures.
