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Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium

OBJECTIVES: Artificial intelligence (AI) applications in neurosurgery have an increasing momentum as well as the growing number of implementations in the medical literature. In recent years, AI research define a link between neuroscience and AI. It is a connection between knowing and understanding t...

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Autores principales: Dundar, Tolga Turan, Yurtsever, Ismail, Pehlivanoglu, Meltem Kurt, Yildiz, Ugur, Eker, Aysegul, Demir, Mehmet Ali, Mutluer, Ahmet Serdar, Tektaş, Recep, Kazan, Mevlude Sila, Kitis, Serkan, Gokoglu, Abdulkerim, Dogan, Ihsan, Duru, Nevcihan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099011/
https://www.ncbi.nlm.nih.gov/pubmed/35574559
http://dx.doi.org/10.3389/fsurg.2022.863633
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author Dundar, Tolga Turan
Yurtsever, Ismail
Pehlivanoglu, Meltem Kurt
Yildiz, Ugur
Eker, Aysegul
Demir, Mehmet Ali
Mutluer, Ahmet Serdar
Tektaş, Recep
Kazan, Mevlude Sila
Kitis, Serkan
Gokoglu, Abdulkerim
Dogan, Ihsan
Duru, Nevcihan
author_facet Dundar, Tolga Turan
Yurtsever, Ismail
Pehlivanoglu, Meltem Kurt
Yildiz, Ugur
Eker, Aysegul
Demir, Mehmet Ali
Mutluer, Ahmet Serdar
Tektaş, Recep
Kazan, Mevlude Sila
Kitis, Serkan
Gokoglu, Abdulkerim
Dogan, Ihsan
Duru, Nevcihan
author_sort Dundar, Tolga Turan
collection PubMed
description OBJECTIVES: Artificial intelligence (AI) applications in neurosurgery have an increasing momentum as well as the growing number of implementations in the medical literature. In recent years, AI research define a link between neuroscience and AI. It is a connection between knowing and understanding the brain and how to simulate the brain. The machine learning algorithms, as a subset of AI, are able to learn with experiences, perform big data analysis, and fulfill human-like tasks. Intracranial surgical approaches that have been defined, disciplined, and developed in the last century have become more effective with technological developments. We aimed to define individual-safe, intracranial approaches by introducing functional anatomical structures and pathological areas to artificial intelligence. METHODS: Preoperative MR images of patients with deeply located brain tumors were used for planning. Intracranial arteries, veins, and neural tracts are listed and numbered. Voxel values of these selected regions in cranial MR sequences were extracted and labeled. Tumor tissue was segmented as the target. Q-learning algorithm which is a model-free reinforcement learning algorithm was run on labeled voxel values (on optimal paths extracted from the new heuristic-based path planning algorithm), then the algorithm was assigned to list the cortico-tumoral pathways that aim to remove the maximum tumor tissue and in the meantime that functional anatomical tissues will be least affected. RESULTS: The most suitable cranial entry areas were found with the artificial intelligence algorithm. Cortico-tumoral pathways were revealed using Q-learning from these optimal points. CONCLUSIONS: AI will make a significant contribution to the positive outcomes as its use in both preoperative surgical planning and intraoperative technique equipment assisted neurosurgery, its use increased.
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spelling pubmed-90990112022-05-14 Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium Dundar, Tolga Turan Yurtsever, Ismail Pehlivanoglu, Meltem Kurt Yildiz, Ugur Eker, Aysegul Demir, Mehmet Ali Mutluer, Ahmet Serdar Tektaş, Recep Kazan, Mevlude Sila Kitis, Serkan Gokoglu, Abdulkerim Dogan, Ihsan Duru, Nevcihan Front Surg Surgery OBJECTIVES: Artificial intelligence (AI) applications in neurosurgery have an increasing momentum as well as the growing number of implementations in the medical literature. In recent years, AI research define a link between neuroscience and AI. It is a connection between knowing and understanding the brain and how to simulate the brain. The machine learning algorithms, as a subset of AI, are able to learn with experiences, perform big data analysis, and fulfill human-like tasks. Intracranial surgical approaches that have been defined, disciplined, and developed in the last century have become more effective with technological developments. We aimed to define individual-safe, intracranial approaches by introducing functional anatomical structures and pathological areas to artificial intelligence. METHODS: Preoperative MR images of patients with deeply located brain tumors were used for planning. Intracranial arteries, veins, and neural tracts are listed and numbered. Voxel values of these selected regions in cranial MR sequences were extracted and labeled. Tumor tissue was segmented as the target. Q-learning algorithm which is a model-free reinforcement learning algorithm was run on labeled voxel values (on optimal paths extracted from the new heuristic-based path planning algorithm), then the algorithm was assigned to list the cortico-tumoral pathways that aim to remove the maximum tumor tissue and in the meantime that functional anatomical tissues will be least affected. RESULTS: The most suitable cranial entry areas were found with the artificial intelligence algorithm. Cortico-tumoral pathways were revealed using Q-learning from these optimal points. CONCLUSIONS: AI will make a significant contribution to the positive outcomes as its use in both preoperative surgical planning and intraoperative technique equipment assisted neurosurgery, its use increased. Frontiers Media S.A. 2022-04-29 /pmc/articles/PMC9099011/ /pubmed/35574559 http://dx.doi.org/10.3389/fsurg.2022.863633 Text en Copyright © 2022 Dundar, Yurtsever, Pehlivanoglu, Yildiz, Eker, Demir, Mutluer, Tektaş, Kazan, Kitis, Gokoglu, Dogan and Duru. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Surgery
Dundar, Tolga Turan
Yurtsever, Ismail
Pehlivanoglu, Meltem Kurt
Yildiz, Ugur
Eker, Aysegul
Demir, Mehmet Ali
Mutluer, Ahmet Serdar
Tektaş, Recep
Kazan, Mevlude Sila
Kitis, Serkan
Gokoglu, Abdulkerim
Dogan, Ihsan
Duru, Nevcihan
Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium
title Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium
title_full Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium
title_fullStr Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium
title_full_unstemmed Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium
title_short Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium
title_sort machine learning-based surgical planning for neurosurgery: artificial intelligent approaches to the cranium
topic Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099011/
https://www.ncbi.nlm.nih.gov/pubmed/35574559
http://dx.doi.org/10.3389/fsurg.2022.863633
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