  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.
