Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning

TL;DR

This paper introduces a deep learning model for automated, objective, and reproducible video-based assessment of surgical skills, enabling both formative feedback and high-stakes summative evaluation to improve surgical training and certification.

Contribution

The study presents a novel deep learning approach that automates surgical skill assessment from videos, incorporating heatmaps for formative feedback and enabling high-stakes evaluation.

Findings

Automated assessment correlates with expert evaluations.

Heatmaps provide visual insights into surgical performance.

Model facilitates scalable and objective surgical training evaluation.

Abstract

To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated - none of which is currently achievable. Video-based assessment (VBA) is being deployed in intraoperative and simulation settings to evaluate technical skill execution. However, VBA remains manually- and time-intensive and prone to subjective interpretation and poor inter-rater reliability. Herein, we propose a deep learning (DL) model that can automatically and objectively provide a high-stakes summative assessment of surgical skill execution based on video feeds and low-stakes formative assessment to guide surgical skill acquisition. Formative assessment is generated using heatmaps of visual features that correlate with surgical performance. Hence, the DL model paves the way to the quantitative and reproducible evaluation of surgical tasks from videos with the potential for broad dissemination in surgical training, certification, and credentialing.