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Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview
Intracranial aneurysms (IAs) are a significant public health concern. In populations without comorbidity and a mean age of 50 years, their prevalence is up to 3.2%. An efficient method for identifying subjects at high risk of an IA is warranted to provide adequate radiological screening guidelines a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904392/ https://www.ncbi.nlm.nih.gov/pubmed/35280303 http://dx.doi.org/10.3389/fneur.2022.784326 |
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author | Marasini, Anurag Shrestha, Alisha Phuyal, Subash Zaidat, Osama O. Kalia, Junaid Siddiq |
author_facet | Marasini, Anurag Shrestha, Alisha Phuyal, Subash Zaidat, Osama O. Kalia, Junaid Siddiq |
author_sort | Marasini, Anurag |
collection | PubMed |
description | Intracranial aneurysms (IAs) are a significant public health concern. In populations without comorbidity and a mean age of 50 years, their prevalence is up to 3.2%. An efficient method for identifying subjects at high risk of an IA is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. Artificial intelligence (AI) has received worldwide attention for its impressive performance in image-based tasks. It can serve as an adjunct to physicians in clinical settings, improving diagnostic accuracy while reducing physicians' workload. AI can perform tasks such as pattern recognition, object identification, and problem resolution with human-like intelligence. Based on the data collected for training, AI can assist in decisions in a semi-autonomous manner. Similarly, AI can identify a likely diagnosis and also, select a suitable treatment based on health records or imaging data without any explicit programming (instruction set). Aneurysm rupture prediction is the holy grail of prediction modeling. AI can significantly improve rupture prediction, saving lives and limbs in the process. Nowadays, deep learning (DL) has shown significant potential in accurately detecting lesions on medical imaging and has reached, or perhaps surpassed, an expert-level of diagnosis. This is the first step to accurately diagnose UIAs with increased computational radiomicis. This will not only allow diagnosis but also suggest a treatment course. In the future, we will see an increasing role of AI in both the diagnosis and management of IAs. |
format | Online Article Text |
id | pubmed-8904392 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89043922022-03-10 Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview Marasini, Anurag Shrestha, Alisha Phuyal, Subash Zaidat, Osama O. Kalia, Junaid Siddiq Front Neurol Neurology Intracranial aneurysms (IAs) are a significant public health concern. In populations without comorbidity and a mean age of 50 years, their prevalence is up to 3.2%. An efficient method for identifying subjects at high risk of an IA is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. Artificial intelligence (AI) has received worldwide attention for its impressive performance in image-based tasks. It can serve as an adjunct to physicians in clinical settings, improving diagnostic accuracy while reducing physicians' workload. AI can perform tasks such as pattern recognition, object identification, and problem resolution with human-like intelligence. Based on the data collected for training, AI can assist in decisions in a semi-autonomous manner. Similarly, AI can identify a likely diagnosis and also, select a suitable treatment based on health records or imaging data without any explicit programming (instruction set). Aneurysm rupture prediction is the holy grail of prediction modeling. AI can significantly improve rupture prediction, saving lives and limbs in the process. Nowadays, deep learning (DL) has shown significant potential in accurately detecting lesions on medical imaging and has reached, or perhaps surpassed, an expert-level of diagnosis. This is the first step to accurately diagnose UIAs with increased computational radiomicis. This will not only allow diagnosis but also suggest a treatment course. In the future, we will see an increasing role of AI in both the diagnosis and management of IAs. Frontiers Media S.A. 2022-02-23 /pmc/articles/PMC8904392/ /pubmed/35280303 http://dx.doi.org/10.3389/fneur.2022.784326 Text en Copyright © 2022 Marasini, Shrestha, Phuyal, Zaidat and Kalia. 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 | Neurology Marasini, Anurag Shrestha, Alisha Phuyal, Subash Zaidat, Osama O. Kalia, Junaid Siddiq Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview |
title | Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview |
title_full | Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview |
title_fullStr | Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview |
title_full_unstemmed | Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview |
title_short | Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview |
title_sort | role of artificial intelligence in unruptured intracranial aneurysm: an overview |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904392/ https://www.ncbi.nlm.nih.gov/pubmed/35280303 http://dx.doi.org/10.3389/fneur.2022.784326 |
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