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A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework
Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic proces...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562430/ https://www.ncbi.nlm.nih.gov/pubmed/37813914 http://dx.doi.org/10.1038/s41598-023-43478-z |
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author | Ali, Mohamed T. Gebreil, Ahmad ElNakieb, Yaser Elnakib, Ahmed Shalaby, Ahmed Mahmoud, Ali Sleman, Ahmed Giridharan, Guruprasad A. Barnes, Gregory Elbaz, Ayman S. |
author_facet | Ali, Mohamed T. Gebreil, Ahmad ElNakieb, Yaser Elnakib, Ahmed Shalaby, Ahmed Mahmoud, Ali Sleman, Ahmed Giridharan, Guruprasad A. Barnes, Gregory Elbaz, Ayman S. |
author_sort | Ali, Mohamed T. |
collection | PubMed |
description | Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area. |
format | Online Article Text |
id | pubmed-10562430 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105624302023-10-11 A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework Ali, Mohamed T. Gebreil, Ahmad ElNakieb, Yaser Elnakib, Ahmed Shalaby, Ahmed Mahmoud, Ali Sleman, Ahmed Giridharan, Guruprasad A. Barnes, Gregory Elbaz, Ayman S. Sci Rep Article Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area. Nature Publishing Group UK 2023-10-09 /pmc/articles/PMC10562430/ /pubmed/37813914 http://dx.doi.org/10.1038/s41598-023-43478-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ali, Mohamed T. Gebreil, Ahmad ElNakieb, Yaser Elnakib, Ahmed Shalaby, Ahmed Mahmoud, Ali Sleman, Ahmed Giridharan, Guruprasad A. Barnes, Gregory Elbaz, Ayman S. A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework |
title | A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework |
title_full | A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework |
title_fullStr | A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework |
title_full_unstemmed | A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework |
title_short | A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework |
title_sort | personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562430/ https://www.ncbi.nlm.nih.gov/pubmed/37813914 http://dx.doi.org/10.1038/s41598-023-43478-z |
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