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Using Artificial Intelligence to Gauge Competency on a Novel Laparoscopic Training System.

C Yoonhee Ryder1, Nicole M Mott1, Christopher L Gross2

  • 1University of Michigan Medical School, Ann Arbor, Michigan.

Journal of Surgical Education
|December 30, 2023
PubMed
Summary

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Artificial intelligence (AI) shows moderate correlation in assessing laparoscopic surgical skills, offering a potential solution for training in low- and middle-income countries (LMICs). This AI-powered tool can help bridge educational gaps where traditional training is limited.

Area of Science:

  • Medical Education
  • Surgical Simulation
  • Artificial Intelligence in Medicine

Background:

  • Laparoscopic surgical skill assessment tools and machine learning are often inaccessible in low- and middle-income countries (LMICs).
  • There is a need for accessible, objective methods to train and assess psychomotor skills in laparoscopic surgery within resource-limited settings.

Purpose of the Study:

  • To assess the validity of artificial intelligence (AI) generated scoring measures for laparoscopic simulation videos.
  • To compare the accuracy of AI-derived surgical skill scores with human-generated scores.

Main Methods:

  • Seventy-four laparoscopic salpingostomy simulation videos from novices and experts were collected.
  • Human participants graded videos using a modified Objective Structured Assessment of Technical Skills (OSATS).
Keywords:
artificial intelligenceglobal surgerylaparoscopic salpingostomymachine learningsurgical education

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  • AI analyzed videos using path length, dimensionless jerk, and standard deviation of tool position, with scores generated via 5-fold cross-validation and K-Nearest Neighbors.

    • Main Results:

    • AI demonstrated 65-77% complete accuracy compared to human assessment.
    • Moderate correlation (ICC=0.72) was found between AI and human scores for the total summed score.
    • No significant differences in rank mean scores were observed for Flow of Operation, Respect for Tissue, and Economy of Motion.

    Conclusions:

    • AI video review of global surgical characteristics is comparable to human review in laparoscopic training systems.
    • Machine learning holds potential to address educational disparities in LMICs by providing feasible training and assessment solutions.
    • AI-driven assessment can supplement or provide an alternative to traditional apprenticeship models in surgical education.