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Automated assessment of laparoscopic pattern cutting skills using computer vision and deep learning
Fuat Uyguroglu1, William Joseph Hoy1, Adam Meyers1
1Department of Industrial and Systems Engineering, University of Miami, Coral Gables, FL.
Surgery
|July 11, 2025
Summary
This study introduces an automated system for assessing surgical pattern cutting skills, improving objectivity and efficiency over manual methods. The machine learning tool provides accurate, quantitative feedback, enhancing surgical training and evaluation.
Area of Science:
- Surgical Education
- Medical Simulation
- Computer Vision in Medicine
Background:
- Current pattern cutting assessment in Fundamentals of Laparoscopic Surgery (FLS) is manual, leading to time-consuming evaluations prone to errors.
- Objective and automated assessment systems are needed to improve the efficiency, reliability, and standardization of surgical skills evaluation.
Purpose of the Study:
- To develop and evaluate a machine learning-enhanced computer vision system for automated pattern cutting assessment in surgical training.
Main Methods:
- A computer vision system was developed using a You Only Look Once (YOLO)-based deep learning model for specimen segmentation.
- The system analyzes digital images of cut specimens, comparing them against circular targets and measuring area-based and radial deviations.
- Performance was validated using synthetic test samples and real surgical specimens.
Main Results:
- The segmentation model achieved 98.32% accuracy on synthetic samples with a mean absolute error of 34.7 mm².
- The system accurately measured deviations in real surgical specimens, with results closely matching expert evaluations.
- Quantitative feedback is provided via color-coded visualizations and radial deviation analysis.
Conclusions:
- The automated system offers objective, quantitative evaluation for pattern cutting skills, suitable for standardized implementation in FLS testing.
- Despite challenges with specimen distortion, the system's speed and reduced reliance on human assessment make it a valuable tool for surgical skills evaluation and training.
