Objective assessment of robotic suturing skills with a new computerized system: A step forward in the training of robotic surgeons

Caleb Busch, Ryu Nakadate, Munenori Uemura, Satoshi Obata, Takahiro Jimbo, Makoto Hashizume

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

INTRODUCTION: The purpose of this study was to assess robot-assisted suturing skills on a laparoscopic intestinal anastomosis model by using a novel computerized objective assessment system. METHODS: This study compared the suturing skills of 13 surgically naïve participants on an artificial intestinal anastomosis model that mimics real tissue. Each examinee sutured using da Vinci robot assistance under 2-D and 3-D visualization and with conventional laparoscopy (CL). Pressure-measuring and image-processing devices were employed to quantitatively evaluate suturing skills. Five unique criteria were used to evaluate the skills of participants. RESULTS: Suturing under 3-D visualization (P < 0.01) and with CL (P < 0.05) were significantly faster than under 2-D visualization. Sutures placed under 3-D (P < 0.05) and 2-D (P < 0.01) visualization had significantly better suture tension than those placed with CL, which did not meet acceptable values for suture tension. Sutures placed with CL had significantly better air pressure leakage than those placed under 2-D visualization (P < 0.05), which did not meet acceptable values for air pressure leakage and wound opening area. One participants failed to achieve full-thickness sutures with 2-D, two participants with CL, and one participant with 3-D. CONCLUSION: Using 3-D vision is necessary for complex maneuvering during robot-assisted minimally invasive surgery. Our quantitative assessment system is useful for evaluating the skill acquisition of surgeon-trainees undergoing robotic surgery training.

Original languageEnglish
Pages (from-to)388-395
Number of pages8
JournalAsian journal of endoscopic surgery
Volume12
Issue number4
DOIs
Publication statusPublished - Oct 1 2019

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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