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Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms
Author(s) -
VillanuevaNaquid Iván,
SoubervielleMontalvo Carlos,
AguilarPonce Ruth M.,
TovarArriaga Saúl,
CuevasTello Juan C.,
PuenteMontejano Cesar A.,
MejiaCarlos Marcela,
TorresCorzo Jaime G.
Publication year - 2020
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.2060
Subject(s) - keyhole , trajectory , computer science , voxel , set (abstract data type) , genetic algorithm , neurosurgery , algorithm , medicine , artificial intelligence , surgery , machine learning , physics , materials science , astronomy , welding , metallurgy , programming language
Background: Preoperative assessment to find the safest trajectory in keyhole neurosurgery can reduce post operative complications. Methods : We introduced a novel preoperative risk assessment semiautomated methodology based on the sum of N maximum risk values using a generic genetic algorithm for the safest trajectory search. Results: A set of candidates trajectories were found for two surgical procedures. The trajectories search is done using a risk map considering the proximity of voxels within risk structures in multiple points and a genetic algorithm to avoid an exhaustive search. The trajectories were validated by a group of neurosurgeons. Conclusions : The trajectories obtained with the proposal method were shorter in 5% and have greater distance from the voxels within the blood vessels in 4.7%. The use of genetic algorithm (GA) speeds up the search for the safest trajectory, decreasing in 99.9% the time required for an exhaustive search.