AI-Driven Evaluation of Surgical Skill via Action Recognition

TL;DR

This paper presents an AI-based system that automates the assessment of surgical microanastomosis skills using advanced video analysis techniques, providing objective and scalable evaluation to improve surgical training.

Contribution

The study introduces a novel AI framework combining a video transformer with motion feature extraction for automated surgical skill assessment, enhancing objectivity and scalability.

Findings

Achieved 87.7% frame-level action segmentation accuracy

Reached 93.62% accuracy after post-processing

Attained 76% classification accuracy in replicating expert assessments

Abstract

The development of effective training and evaluation strategies is critical. Conventional methods for assessing surgical proficiency typically rely on expert supervision, either through onsite observation or retrospective analysis of recorded procedures. However, these approaches are inherently subjective, susceptible to inter-rater variability, and require substantial time and effort from expert surgeons. These demands are often impractical in low- and middle-income countries, thereby limiting the scalability and consistency of such methods across training programs. To address these limitations, we propose a novel AI-driven framework for the automated assessment of microanastomosis performance. The system integrates a video transformer architecture based on TimeSformer, improved with hierarchical temporal attention and weighted spatial attention mechanisms, to achieve accurate action recognition within surgical videos. Fine-grained motion features are then extracted using a YOLO-based object detection and tracking method, allowing for detailed analysis of instrument kinematics. Performance is evaluated along five aspects of microanastomosis skill, including overall action execution, motion quality during procedure-critical actions, and general instrument handling. Experimental validation using a dataset of 58 expert-annotated videos demonstrates the effectiveness of the system, achieving 87.7% frame-level accuracy in action segmentation that increased to 93.62% with post-processing, and an average classification accuracy of 76% in replicating expert assessments across all skill aspects. These findings highlight the system's potential to provide objective, consistent, and interpretable feedback, thereby enabling more standardized, data-driven training and evaluation in surgical education.