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Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the propos...

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Autores principales: Aslam, Nida, Khan, Irfan Ullah, Bashamakh, Asma, Alghool, Fatima A., Aboulnour, Menna, Alsuwayan, Noorah M., Alturaif, Rawa’a K., Brahimi, Samiha, Aljameel, Sumayh S., Al Ghamdi, Kholoud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609137/
https://www.ncbi.nlm.nih.gov/pubmed/36298206
http://dx.doi.org/10.3390/s22207856
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author Aslam, Nida
Khan, Irfan Ullah
Bashamakh, Asma
Alghool, Fatima A.
Aboulnour, Menna
Alsuwayan, Noorah M.
Alturaif, Rawa’a K.
Brahimi, Samiha
Aljameel, Sumayh S.
Al Ghamdi, Kholoud
author_facet Aslam, Nida
Khan, Irfan Ullah
Bashamakh, Asma
Alghool, Fatima A.
Aboulnour, Menna
Alsuwayan, Noorah M.
Alturaif, Rawa’a K.
Brahimi, Samiha
Aljameel, Sumayh S.
Al Ghamdi, Kholoud
author_sort Aslam, Nida
collection PubMed
description Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.
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spelling pubmed-96091372022-10-28 Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities Aslam, Nida Khan, Irfan Ullah Bashamakh, Asma Alghool, Fatima A. Aboulnour, Menna Alsuwayan, Noorah M. Alturaif, Rawa’a K. Brahimi, Samiha Aljameel, Sumayh S. Al Ghamdi, Kholoud Sensors (Basel) Review Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization. MDPI 2022-10-16 /pmc/articles/PMC9609137/ /pubmed/36298206 http://dx.doi.org/10.3390/s22207856 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Aslam, Nida
Khan, Irfan Ullah
Bashamakh, Asma
Alghool, Fatima A.
Aboulnour, Menna
Alsuwayan, Noorah M.
Alturaif, Rawa’a K.
Brahimi, Samiha
Aljameel, Sumayh S.
Al Ghamdi, Kholoud
Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities
title Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities
title_full Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities
title_fullStr Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities
title_full_unstemmed Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities
title_short Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities
title_sort multiple sclerosis diagnosis using machine learning and deep learning: challenges and opportunities
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609137/
https://www.ncbi.nlm.nih.gov/pubmed/36298206
http://dx.doi.org/10.3390/s22207856
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