Video-Based Performance Analysis in Pituitary Surgery - Part 2: Artificial Intelligence Assisted Surgical Coaching

Abstract

Background: Superior surgical skill improves surgical outcomes in endoscopic pituitary adenoma surgery. Video-based coaching programs, pioneered in professional sports, have shown promise in surgical training. In this study, we developed and assessed a video-based coaching program using artificial intelligence (AI) assistance.

Methods: An AI-assisted video-based surgical coaching was implemented over 6 months with the pituitary surgery team. The program consisted of 1) monthly random video analysis and review; and 2) quarterly 2-hour educational meetings discussing these videos and learning points. Each video was annotated for surgical phases and steps using AI, which improved video interactivity and allowed the calculation of quantitative metrics. Primary outcomes were program feasibility, acceptability, and appropriateness. Surgical performance (via modified Objective Structured Assessment of Technical Skills) and early surgical outcomes were recorded for every case during the 6-month coaching period, and a preceding 6-month control period. Beta and logistic regression were used to assess the change in modified Objective Structured Assessment of Technical Skills scores and surgical outcomes after the coaching program implementation.

Results: All participants highly rated the program's feasibility, acceptability, and appropriateness. During the coaching program, 63 endoscopic pituitary adenoma cases were included, with 41 in the control group. Surgical performance across all operative phases improved during the coaching period (P < 0.001), with a reduction in new postoperative anterior pituitary hormone deficit (P = 0.01).

Conclusions: We have developed a novel AI-assisted video surgical coaching program for endoscopic pituitary adenoma surgery - demonstrating its viability and impact on surgical performance. Early results also suggest improvement in patient outcomes. Future studies should be multicenter and longer term.

Keywords: Artificial intelligence; Education; Machine learning; Pituitary adenoma; Training; Transsphenoidal.