Preoperative Planning Algorithm for Robot-Assisted Minimally Invasive Cholecystectomy Combined With Appendectomy

Preoperative planning for robot-assisted minimally invasive surgery is critical stage. Recently, many studies focus on the preoperative planning of the robot-assisted minimally invasive single-site surgery. However, the preoperative planning for the robot-assisted minimally invasive combined surgery...

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Bibliographic Details
Main Authors: Tao Song, Bo Pan, Guojun Niu, Yili Fu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9207900/
Description
Summary:Preoperative planning for robot-assisted minimally invasive surgery is critical stage. Recently, many studies focus on the preoperative planning of the robot-assisted minimally invasive single-site surgery. However, the preoperative planning for the robot-assisted minimally invasive combined surgery based on the optimization algorithm has not been reported. In order to improve the dexterity and coordination of the manipulators in the surgical areas and to reduce the preoperative adjustment time for the combined surgery, this paper proposes a preoperative planning algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) for robot-assisted minimally invasive Cholecystectomy combined with Appendectomy (RAMICA). The preoperative planning algorithm simultaneously optimizes the entry ports and configurations of the manipulators. The optimization objective functions of the preoperative planning algorithm consist of a novel global dexterity index (GDI) based on the coefficient of variation and the coordination index (CI) that reflects hand-eye coordination and instrument coordination. The constraints of the preoperative planning algorithm include the port placement constraint and the non-collision constraint. The preoperative planning scheme based on the optimization algorithm are verified by comparative simulations to provide the better dexterity and coordination of the manipulators. Finally, the contrast experiments are carried out to demonstrate the effectiveness and superiority of the preoperative planning scheme obtained by the optimization algorithm.
ISSN:2169-3536