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A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data
Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6620533/ https://www.ncbi.nlm.nih.gov/pubmed/31333507 http://dx.doi.org/10.3389/fpsyt.2019.00392 |
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author | Dekhil, Omar Ali, Mohamed El-Nakieb, Yaser Shalaby, Ahmed Soliman, Ahmed Switala, Andrew Mahmoud, Ali Ghazal, Mohammed Hajjdiab, Hassan Casanova, Manuel F. Elmaghraby, Adel Keynton, Robert El-Baz, Ayman Barnes, Gregory |
author_facet | Dekhil, Omar Ali, Mohamed El-Nakieb, Yaser Shalaby, Ahmed Soliman, Ahmed Switala, Andrew Mahmoud, Ali Ghazal, Mohammed Hajjdiab, Hassan Casanova, Manuel F. Elmaghraby, Adel Keynton, Robert El-Baz, Ayman Barnes, Gregory |
author_sort | Dekhil, Omar |
collection | PubMed |
description | Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans. |
format | Online Article Text |
id | pubmed-6620533 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-66205332019-07-22 A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data Dekhil, Omar Ali, Mohamed El-Nakieb, Yaser Shalaby, Ahmed Soliman, Ahmed Switala, Andrew Mahmoud, Ali Ghazal, Mohammed Hajjdiab, Hassan Casanova, Manuel F. Elmaghraby, Adel Keynton, Robert El-Baz, Ayman Barnes, Gregory Front Psychiatry Psychiatry Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans. Frontiers Media S.A. 2019-07-04 /pmc/articles/PMC6620533/ /pubmed/31333507 http://dx.doi.org/10.3389/fpsyt.2019.00392 Text en Copyright © 2019 Dekhil, Ali, El-Nakieb, Shalaby, Soliman, Switala, Mahmoud, Ghazal, Hajjdiab, Casanova, Elmaghraby, Keynton, El-Baz and Barnes 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 | Psychiatry Dekhil, Omar Ali, Mohamed El-Nakieb, Yaser Shalaby, Ahmed Soliman, Ahmed Switala, Andrew Mahmoud, Ali Ghazal, Mohammed Hajjdiab, Hassan Casanova, Manuel F. Elmaghraby, Adel Keynton, Robert El-Baz, Ayman Barnes, Gregory A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data |
title | A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data |
title_full | A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data |
title_fullStr | A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data |
title_full_unstemmed | A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data |
title_short | A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data |
title_sort | personalized autism diagnosis cad system using a fusion of structural mri and resting-state functional mri data |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6620533/ https://www.ncbi.nlm.nih.gov/pubmed/31333507 http://dx.doi.org/10.3389/fpsyt.2019.00392 |
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