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Technical Skill Impacts the Success of Sequential Robotic Suturing Substeps.
Daniel I Sanford1, Balint Der1, Taseen F Haque1
1Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Journal of Endourology
|November 15, 2021
Summary
Mastering robotic suturing substeps like needle positioning and entry angle is crucial. Ideal performance in early steps significantly improves later steps, enhancing accuracy and efficiency in surgical training.
Area of Science:
- Robotic surgery
- Surgical education
- Surgical skill assessment
Background:
- Robotic surgical performance, particularly suturing, correlates with patient outcomes.
- Suturing can be broken down into substeps: needle positioning, entry angle, driving, and withdrawal.
- Analyzing substeps allows for precise feedback and skill evaluation in surgical training.
Purpose of the Study:
- To evaluate if technical skill in specific suturing substeps influences subsequent substep execution.
- To determine the association between substep technical skill, accuracy, and efficiency.
- To identify inter-relationships within the robotic suturing process.
Main Methods:
- Twenty-two surgeons performed standardized sutures on the Mimic™ Flex virtual reality robotic simulator.
- Suturing videos were analyzed for technical skill scores in four substeps.
- Hierarchical Poisson regression with generalized estimating equations examined associations between substep skill levels.
Main Results:
- Significant associations were found between technical skill scores across suturing substeps.
- Ideal needle positioning improved the likelihood of an ideal needle entry angle (RR=1.12, p=0.05).
- Ideal needle entry angle and driving were associated with ideal needle withdrawal (RR=1.27, p=0.03; RR=1.3, p=0.03).
Conclusions:
- Technical skill in suturing substeps is interconnected, influencing overall performance.
- Mastering individual substeps is vital for improving robotic suturing accuracy and efficiency.
- Findings support targeted training on suturing substeps and inform future machine learning evaluation tools.
