Assessment of surgical skills by using surgical navigation in robot-assisted partial nephrectomy

Satoshi Kobayashi, Byunghyun Cho, Arnaud Huaulmé, Katsunori Tatsugami, Hiroshi Honda, Pierre Jannin, Makoto Hashizumea, Masatoshi Eto

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Purpose: To assess surgical skills in robot-assisted partial nephrectomy (RAPN) with and without surgical navigation (SN). Methods: We employed an SN system that synchronizes the real-time endoscopic image with a virtual reality three-dimensional (3D) model for RAPN and evaluated the skills of two expert surgeons with regard to the identification and dissection of the renal artery (non-SN group, n = 21 [first surgeon n = 9, second surgeon n = 12]; SN group, n = 32 [first surgeon n = 11, second surgeon n = 21]). We converted all movements of the robotic forceps during RAPN into a dedicated vocabulary. Using RAPN videos, we classified all movements of the robotic forceps into direct action (defined as movements of the robotic forceps that directly affect tissues) and connected motion (defined as movements that link actions). In addition, we analyzed the frequency, duration, and occupancy rate of the connected motion. Results: In the SN group, the R.E.N.A.L nephrometry score was lower (7 vs. 6, P = 0.019) and the time to identify and dissect the renal artery (16 vs. 9 min, P = 0.008) was significantly shorter. The connected motions of inefficient “insert,” “pull,” and “rotate” motions were significantly improved by SN. SN significantly improved the frequency, duration, and occupancy rate of connected motions of the right hand of the first surgeon and of both hands of the second surgeon. The improvements in connected motions were positively associated with SN for both surgeons. Conclusion: This is the first study to investigate SN for nephron-sparing surgery. SN with 3D models might help improve the connected motions of expert surgeons to ensure efficient RAPN.

Original languageEnglish
Pages (from-to)1449-1459
Number of pages11
JournalInternational Journal of Computer Assisted Radiology and Surgery
Volume14
Issue number8
DOIs
Publication statusPublished - Aug 1 2019

All Science Journal Classification (ASJC) codes

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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