Automated assessment of laparoscopic pattern cutting skills using computer vision and deep learning

Pattern cutting assessment in Fundamentals of Laparoscopic Surgery currently relies on manual measurement, which can be time-consuming and prone to variability and human error. An automated, objective assessment system could enhance the efficiency, reliability, and standardization of surgical skills evaluation.

We developed a machine learning-enhanced computer vision system that analyzes digital images of cut specimens, comparing them against predefined circular targets. The system uses a You Only Look Once-based deep learning model trained on synthetic data for specimen segmentation, complemented by comprehensive error analysis measuring both area-based and radial deviations. Its performance was evaluated using both synthetic test samples and real surgical specimens.

The segmentation model achieved 98.32% accuracy on synthetic test samples, with a mean absolute error of 34.7 mm². Analysis of real surgical specimens demonstrated the system's ability to measure deviation accurately, with assessments aligning closely with expert surgical evaluations of cutting performance. The system provides detailed quantitative feedback through color-coded visualizations and radial deviation analysis.

The automated assessment system offers objective and quantitative evaluation of pattern cutting skills with potential for standardized implementation across Fundamentals of Laparoscopic Surgery testing centers. Although distortion in the shape of the sample material presents some challenges, the system's comprehensive analysis capabilities, rapid processing, and reduced need for human evaluation make it a promising tool for surgical skills assessment and training.