Date of Award

5-2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Bioengineering

Committee Chair/Advisor

Dr. Joseph Singapogu

Committee Member

Dr. Richard E. Groff

Committee Member

Dr. Jeremy Mercuri

Abstract

To enhance patient safety, surgical education is increasingly incorporating simulation for formative skills assessment and training. However, many standardized assessment tools rely on human raters for performance assessment, which is resource-intensive and subjective. Simulators that provide automated and objective metrics from sensor data can address this limitation. This thesis presents an instrumented bench suturing simulator, patterned after the Clock Face (CF) radial suturing model from the Fundamentals of Vascular Surgery (FVS), for automated and objective assessment of open suturing skills by particularly focusing on biomechanical analysis of hand movements. For this research, 97 participants (35 attending surgeons and fellows, 32 surgical residents, and 30 novices) were recruited at national vascular conferences. Automated hand motion metrics, especially focusing on rotational motion analysis, were developed from the Inertial Measurement Unit (IMU) attached to participants’ hands, and the proposed suite of metrics was used to differentiate between skill levels of the three groups. Further, the application of functional data analysis (FDA) to hand roll velocity during radial suturing on the SutureCoach bench simulator for evaluating open suturing performance was studied. By treating temporal sensor data as mathematical functions, FDA provides a holistic view of the dynamic changes in hand roll, offering comprehensive assessments that are easily interpretable and clinically relevant. Cluster analysis was performed on the hand roll profile of subjects, and the relationship between cluster membership and suturing skills was corroborated using proxy measures of skill, such as expert Global Rating Scale evaluation, clinical status, and specific simulator metrics. This thesis provides evidence for the effectiveness of rotational motion analysis for assessing suturing skills. The IMU-based hand motion metrics introduced in this study allow for incorporating hand movement data for suturing skill assessment. Moreover, the clinical relevance of our results extends to the broader field of surgical skill assessment and training, as analyses of this thesis are scalable and adaptable to a wide range of surgical tasks beyond suturing.

Author ORCID Identifier

https://orcid.org/0000-0002-8578-0266

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