Date of Award
5-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Bioengineering
Committee Chair/Advisor
Dr. Ravikiran Singapogu
Committee Member
Dr. Richard E. Groff
Committee Member
Dr. Joe Bible
Committee Member
Dr. John F. Eidt
Committee Member
Dr. Jordon Gilmore
Abstract
Vascular surgery demands highly skilled suturing to manipulate and connect delicate vasculature. However, inadequate methods for open suturing skills training necessitate efficient and objective methods to develop skills. To address this need, medical training simulators for objective surgical skills training are gaining popularity for their formative assessment.
This research details the development of a system to classify suturing performance and provide feedback on a suturing skills measurement and feedback platform (called the SutureCoach). The SutureCoach simulator offers a comprehensive assessment of skill through sensors measuring needle driver motions, membrane forces and torques, subcutaneous suture needle movement, and hand motions. We analyzed suturing trial performance on an extensive dataset of 97 subjects with varying clinical expertise. This research will first focus on the development of sensor-based metrics derived from needle driver motions and membrane forces and torque. These metrics successfully differentiated group metric scores between novices (no medical experience), intermediates (residents), and experts (attending surgeons/fellows). To further validate sensor metrics’ relevance, these metrics were compared with expert assessments of SutureCoach trials, including metrics derived from suture needle movement and hand motions. Several metrics that effectively differentiated group scores also demonstrated significant associations with expert assessment.
These results were incorporated into a machine learning algorithm to classify performance. The developed algorithm then presents a proof-of-concept method to provide feedback based on specific suture performance to the user. This research emphasizes the importance of comprehensive, multi-modal skill assessment for a more holistic evaluation of suturing.
Recommended Citation
Singh, Simar, "Development of Advanced Simulator-based Metrics for Suturing Skills Assessment and Feedback" (2024). All Dissertations. 3574.
https://tigerprints.clemson.edu/all_dissertations/3574
Author ORCID Identifier
0009-0001-8755-6866