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Updated: Sep 10, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Artificial intelligence for automatic FLS credentialing: highlighting and addressing current limitations.

Luca Sestini^1,2, Mai Harris³, Deepak Alapatt^1,2

¹University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France.

Surgical Endoscopy

|August 27, 2025

Summary

This summary is machine-generated.

This study introduces a novel AI approach for assessing surgical skills, significantly improving accuracy in evaluating the Fundamentals of Laparoscopic Surgery (FLS) peg transfer task. The AI system, enhanced with post-processing, achieves high reliability for automated surgical training and credentialing.

Keywords:

Artificial intelligence Computer vision Credentialing Proctoring Simulation Video-based assessment

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Area of Science:

Medical Education
Artificial Intelligence in Surgery
Surgical Skills Assessment

Background:

Artificial Intelligence (AI) offers potential for automating technical skills assessment in surgical training.
AI-driven credentialing for the Fundamentals of Laparoscopic Surgery (FLS) could improve consistency and reduce proctoring costs.
Existing AI studies lack the reliability for high-stakes assessments due to limited validation and modeling.

Purpose of the Study:

To develop and validate a novel AI-based system for assessing the FLS peg transfer task using video recordings.
To enhance the reliability and accuracy of AI models for surgical skills evaluation through advanced post-processing techniques.
To establish new metrics for measuring the AI system's ability to replicate expert human evaluation.

Main Methods:

A novel AI approach analyzes video frames to track object states and classify surgical actions during the FLS peg transfer task.
Multiple AI models generate predictions, refined by two post-processing techniques for temporal consistency and task accuracy.
Introduced 'transfer precision' and 'transfer recall' metrics to evaluate the AI's replication of proctor-level assessments.

Main Results:

Individual AI models achieved high accuracy (>99%) for peg state prediction and good performance (f1-score >78%) for action recognition.
Without post-processing, transfer precision and recall were low (22.86% and 55.56%).
With proposed post-processing, transfer precision and recall significantly improved to 80.44% and 96.51%, respectively.

Conclusions:

Task-specific validation and modeling are crucial for developing reliable AI systems for automated credentialing.
The proposed AI framework and metrics offer generalizable guidelines for reliable AI-based assessment tools in surgical training.
This AI system demonstrates potential for robust evaluation in both simulated and real surgical environments.