AI-driven Automation of End-to-end Assessment of Suturing Expertise
This work addresses the need for efficient and objective assessment of suturing skills in surgical training, though it is incremental as it automates an existing tool rather than introducing a new paradigm.
The paper tackles the problem of automating the End-to-end Assessment of Suturing Expertise (EASE) by developing an AI-based approach to replace time-consuming human scoring, enabling real-time score prediction and feedback for surgical trainees. The result is a system that assesses 7 domains across 3 suturing phases, such as needle handling and driving, to potentially accelerate learning and improve patient outcomes.
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.1 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. In this study, we focus on the following 7 EASE domains that come under 3 suturing phases: 1) Needle Handling: Number of Repositions, Needle Hold Depth, Needle Hold Ratio, and Needle Hold Angle; 2) Needle Driving: Driving Smoothness, and Wrist Rotation; 3) Needle Withdrawal: Wrist Rotation.