Date of Award
8-2019
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical and Computer Engineering (Holcomb Dept. of)
Committee Member
Richard E. Groff, Committee Chair
Committee Member
Ravikiran B. Singapogu
Committee Member
Adam Hoover
Committee Member
Ian D. Walker
Abstract
Suturing is a fundamental surgical skill required in a variety of operations, ranging from wound repair to delicate vascular reconstruction. It is essential that surgeons master requisite suturing skills so that he or she can deliver safe and effective care to patients. Due to an increased emphasis on standardized medical training, tools and methods are needed to provide objective assessment and feedback during the learning process. In this thesis, a new surgical simulator for assessment and training of open surgery suturing skill is introduced. The suturing simulator system design, force-based, motion-based, image-based and image-enabled metrics for skill assessment, and a preliminary study of resident and attending surgeons are presented.
The simulator collects synchronized force, motion, video and touch data during radial continuous suturing. The synchronized data is used to extract metrics for suturing skill assessment. The simulator has a camera positioned underneath the suturing membrane, enabling visual tracking of the needle during suturing. Needle tracking data enables extraction of meaningful metrics for both the process and the product of the suturing task. To better simulate surgical conditions, the height of the simulator and the depth of the membrane are both adjustable.
The metrics were motivated by insight provided to us from practicing vascular surgeons. These metrics were based on the physics of needle insertion forces, the maxim to follow the curve of the needle while driving through tissue, and minimizing lateral forces and motions that induce tear.
Experimental data from a study involving subjects with various levels of suturing expertise (attending surgeons and surgery residents) are presented. Analysis shows forcebased metrics (absolute maximum force/torque in z-direction), motion-based metrics (yaw, pitch, roll), a physical contact metric, image-based metrics (Stitch Length, Idle Time, Needle Tip Trace Distance, Needle Swept Area, Needle Tip Area and Needle Sway Length) and image-enabled metrics (orthogonal force, tangential force and entry angle) are statistically significant in differentiating suturing skill between attendings and residents.
The results suggest that this simulator and accompanying metrics can be used to assess open surgery suturing skill. Furthermore, analysis shows that 6 of 9 image-based metrics were effective in capturing fine-grain differences in skill level between residents and attendings. Moreover, image-based process metrics may be represented graphically in a manner conducive to training. Image-based metrics especially lend themselves to intuitive visualizations. The combination of fine-grained skill differentiation, the ability to simulate depth of suturing, and the intuitive visualizations of selected image-based metrics makes the suturing simulator and associated suite of metrics well-suited for suturing skills assessment and training.
Recommended Citation
Kil, Irfan, "Development and Preliminary Validation of Image-enabled Process Metrics for Assessment of Open Surgery Suturing Skill" (2019). All Dissertations. 2462.
https://open.clemson.edu/all_dissertations/2462