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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9099011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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