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

• Published in: Journal of surgical education Vol. 81; no. 7; pp. 983 - 993
• Main Authors: Kil, Irfan, Eidt, John F., Singapogu, Ravikiran B., Groff, Richard E.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2024
• Subjects: Algorithms; Clinical Competence; Education, Medical, Graduate - methods; Educational Measurement; Humans; image-based metrics; Internship and Residency; medical simulator; objective metrics; open surgery; Simulation Training - methods; skill assessment; Suture Techniques - education; suturing; objective metrics; skill assessment; image-based metrics; Medical Knowledge; open surgery; suturing; medical simulator; Systems-based Practice; and Practice-based Learning and Improvement
• ISSN: 1931-7204; 1878-7452; 1878-7452
• DOI: 10.1016/j.jsurg.2024.03.020

•This paper presents a computer vision algorithm for extraction of image-based metrics for suturing skill assessment•A suturing simulator that adapts the radial suturing task from the Fundamentals of Vascular Surgery (FVS) skills assessment is used to collect data.•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.”•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.•These results demonstrate the potential of image-based metrics for assessment and training of suturing skill in open surgery. 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. 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.” 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. 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.