Assessment of Open Surgery Suturing Skill: Image-based Metrics Using Computer Vision.

Objective: This paper presents a computer vision algorithm for extraction of image-based metrics for suturing skill assessment and the corresponding results from an experimental study of resident and attending surgeons.
Design: A suturing simulator that adapts the radial suturing task from the Fundamentals of Vascular Surgery (FVS) skills assessment is used to collect data. The simulator includes a camera positioned under the suturing membrane, which records needle and thread movement during the suturing task. A computer vision algorithm processes the video data and extracts objective metrics inspired by expert surgeons' recommended best practice, to "follow the curvature of the needle."
Participants and Results: Experimental data from a study involving subjects with various levels of suturing expertise (attending surgeons and surgery residents) are presented. Analysis shows that attendings and residents had statistically different performance on 6 of 9 image-based metrics, including the four new metrics introduced in this paper: Needle Tip Path Length, Needle Swept Area, Needle Tip Area and Needle Sway Length.
Conclusion and Significance: These image-based process metrics may be represented graphically in a manner conducive to training. The results demonstrate the potential of image-based metrics for assessment and training of suturing skill in open surgery.
(Copyright © 2024 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)