Proprietary platform artificial intelligence-based segmentation of surgical videos facilitates review and rating of operative recordings.

BACKGROUND: Implementation of artificial intelligence (AI) in surgery may revolutionize assessment of operative techniques through video capture, auto-segmentation and auto-analysis. The aim of this study was to utilize recordings of a highly standardized surgical intervention, testing the ability of commercially available AI software to auto-segment operative footage in a clinically meaningful way. METHODS: Twenty-five critical steps in liver graft back-table preparations for connection to a commercially available normothermic perfusion liver pump were determined. Five operative videos were captured manually edited. In addition, using the AI-based GoPro auto-edit feature, the last two recordings were auto-segmented for separate analysis and comparison against the manually edited footage. Each manual and auto-segmented video was assessed for: a) inclusion of each one of the 25 steps, (b) technical performance and completion. The assessment was conducted by an independent experienced transplant surgeon using a modified Likert scale (0-not captured, 1-poor, 2-average, 3-excellent). RESULTS: Reviewing and rating AI auto-segmented videos was 16 times faster compared to raw video review. Reviewing and rating AI auto-segmented videos was 96 times faster compared to manually segmented recordings, accounting for time needed to edit raw videos. The average number of steps captured between both raw videos and the manually edited videos was 22.5. The average number of steps captured across the AI auto-segmented videos was 19.5 (87%). The average technical grade for the raw and the manually edited videos was 2.58 ± 0.71, and 2.14 ± 0.99 for the AI auto-segmented videos, denoting a rating congruency of 82.9%. CONCLUSION: AI-based auto-segmentation is an attractive tool for assessment of surgical performance, as it significantly expedites the editing process to prepare footage for review by subject matter experts. To which extent this approach will be applicable outside highly standardized surgical interventions must be further studied.