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Machine learning applications in stroke medicine: advancements, challenges, and future prospectives
Stroke is a leading cause of disability and mortality worldwide, necessitating the development of advanced technologies to improve its diagnosis, treatment, and patient outcomes. In recent years, machine learning techniques have emerged as promising tools in stroke medicine, enabling efficient analy...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10664112/ https://www.ncbi.nlm.nih.gov/pubmed/37843210 http://dx.doi.org/10.4103/1673-5374.382228 |
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author | Daidone, Mario Ferrantelli, Sergio Tuttolomondo, Antonino |
author_facet | Daidone, Mario Ferrantelli, Sergio Tuttolomondo, Antonino |
author_sort | Daidone, Mario |
collection | PubMed |
description | Stroke is a leading cause of disability and mortality worldwide, necessitating the development of advanced technologies to improve its diagnosis, treatment, and patient outcomes. In recent years, machine learning techniques have emerged as promising tools in stroke medicine, enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches. This abstract provides a comprehensive overview of machine learning’s applications, challenges, and future directions in stroke medicine. Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine. Machine learning models have demonstrated remarkable accuracy in imaging analysis, diagnosing stroke subtypes, risk stratifications, guiding medical treatment, and predicting patient prognosis. Despite the tremendous potential of machine learning in stroke medicine, several challenges must be addressed. These include the need for standardized and interoperable data collection, robust model validation and generalization, and the ethical considerations surrounding privacy and bias. In addition, integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care. Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis, tailored treatment selection, and improved prognostication. Continued research and collaboration among clinicians, researchers, and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care, ultimately leading to enhanced patient outcomes and quality of life. This review aims to summarize all the current implications of machine learning in stroke diagnosis, treatment, and prognostic evaluation. At the same time, another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease. |
format | Online Article Text |
id | pubmed-10664112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Wolters Kluwer - Medknow |
record_format | MEDLINE/PubMed |
spelling | pubmed-106641122023-08-14 Machine learning applications in stroke medicine: advancements, challenges, and future prospectives Daidone, Mario Ferrantelli, Sergio Tuttolomondo, Antonino Neural Regen Res Review Stroke is a leading cause of disability and mortality worldwide, necessitating the development of advanced technologies to improve its diagnosis, treatment, and patient outcomes. In recent years, machine learning techniques have emerged as promising tools in stroke medicine, enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches. This abstract provides a comprehensive overview of machine learning’s applications, challenges, and future directions in stroke medicine. Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine. Machine learning models have demonstrated remarkable accuracy in imaging analysis, diagnosing stroke subtypes, risk stratifications, guiding medical treatment, and predicting patient prognosis. Despite the tremendous potential of machine learning in stroke medicine, several challenges must be addressed. These include the need for standardized and interoperable data collection, robust model validation and generalization, and the ethical considerations surrounding privacy and bias. In addition, integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care. Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis, tailored treatment selection, and improved prognostication. Continued research and collaboration among clinicians, researchers, and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care, ultimately leading to enhanced patient outcomes and quality of life. This review aims to summarize all the current implications of machine learning in stroke diagnosis, treatment, and prognostic evaluation. At the same time, another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease. Wolters Kluwer - Medknow 2023-08-14 /pmc/articles/PMC10664112/ /pubmed/37843210 http://dx.doi.org/10.4103/1673-5374.382228 Text en Copyright: © Neural Regeneration Research https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Review Daidone, Mario Ferrantelli, Sergio Tuttolomondo, Antonino Machine learning applications in stroke medicine: advancements, challenges, and future prospectives |
title | Machine learning applications in stroke medicine: advancements, challenges, and future prospectives |
title_full | Machine learning applications in stroke medicine: advancements, challenges, and future prospectives |
title_fullStr | Machine learning applications in stroke medicine: advancements, challenges, and future prospectives |
title_full_unstemmed | Machine learning applications in stroke medicine: advancements, challenges, and future prospectives |
title_short | Machine learning applications in stroke medicine: advancements, challenges, and future prospectives |
title_sort | machine learning applications in stroke medicine: advancements, challenges, and future prospectives |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10664112/ https://www.ncbi.nlm.nih.gov/pubmed/37843210 http://dx.doi.org/10.4103/1673-5374.382228 |
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