Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning
This addresses the need for automated and objective skill assessment in surgical training and certification, though it is incremental as it applies existing deep learning methods to a new domain.
The paper tackled the problem of manual, subjective, and time-intensive surgical skill assessment by proposing a deep learning model that automatically provides both summative and formative assessments from video feeds, achieving objective and reproducible evaluation.
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.