AI-driven Automation of End-to-end Assessment of Suturing Expertise

Affiliations: 1. Cedars Sinai Medical Center, Los Angeles, California 2. University of California Los Angeles, California Keywords: vision transformer, 3D convolutional neural network, assessment tool, suturing skill, video analysis Key information: 1. Research question: Can we automate the end-to-end assessment of suturing expertise, and what benefits would it offer? MANUSCRIPT Introduction We present an AI based approach to automate the End-to-end Assessment of Suturing Expertise (EASE), a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills. While EASE provides granular skills assessment related to suturing to provide trainees with an objective evaluation of their aptitude along with actionable insights, the scoring process is currently performed by human evaluators, which is time and resource consuming. The AI based approach solves this by enabling real-time score prediction with minimal resources during model inference. This enables the possibility of real-time feedback to the surgeons/trainees, potentially accelerating the learning process for the suturing task and mitigating critical errors during the surgery, improving patient outcomes.