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The Role of Structure MRI in Diagnosing Autism

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to b...

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Autores principales: Ali, Mohamed T., ElNakieb, Yaser, Elnakib, Ahmed, Shalaby, Ahmed, Mahmoud, Ali, Ghazal, Mohammed, Yousaf, Jawad, Abu Khalifeh, Hadil, Casanova, Manuel, Barnes, Gregory, El-Baz, Ayman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774643/
https://www.ncbi.nlm.nih.gov/pubmed/35054330
http://dx.doi.org/10.3390/diagnostics12010165
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author Ali, Mohamed T.
ElNakieb, Yaser
Elnakib, Ahmed
Shalaby, Ahmed
Mahmoud, Ali
Ghazal, Mohammed
Yousaf, Jawad
Abu Khalifeh, Hadil
Casanova, Manuel
Barnes, Gregory
El-Baz, Ayman
author_facet Ali, Mohamed T.
ElNakieb, Yaser
Elnakib, Ahmed
Shalaby, Ahmed
Mahmoud, Ali
Ghazal, Mohammed
Yousaf, Jawad
Abu Khalifeh, Hadil
Casanova, Manuel
Barnes, Gregory
El-Baz, Ayman
author_sort Ali, Mohamed T.
collection PubMed
description This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of [Formula: see text]. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.
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spelling pubmed-87746432022-01-21 The Role of Structure MRI in Diagnosing Autism Ali, Mohamed T. ElNakieb, Yaser Elnakib, Ahmed Shalaby, Ahmed Mahmoud, Ali Ghazal, Mohammed Yousaf, Jawad Abu Khalifeh, Hadil Casanova, Manuel Barnes, Gregory El-Baz, Ayman Diagnostics (Basel) Article This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of [Formula: see text]. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism. MDPI 2022-01-11 /pmc/articles/PMC8774643/ /pubmed/35054330 http://dx.doi.org/10.3390/diagnostics12010165 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 Article
Ali, Mohamed T.
ElNakieb, Yaser
Elnakib, Ahmed
Shalaby, Ahmed
Mahmoud, Ali
Ghazal, Mohammed
Yousaf, Jawad
Abu Khalifeh, Hadil
Casanova, Manuel
Barnes, Gregory
El-Baz, Ayman
The Role of Structure MRI in Diagnosing Autism
title The Role of Structure MRI in Diagnosing Autism
title_full The Role of Structure MRI in Diagnosing Autism
title_fullStr The Role of Structure MRI in Diagnosing Autism
title_full_unstemmed The Role of Structure MRI in Diagnosing Autism
title_short The Role of Structure MRI in Diagnosing Autism
title_sort role of structure mri in diagnosing autism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774643/
https://www.ncbi.nlm.nih.gov/pubmed/35054330
http://dx.doi.org/10.3390/diagnostics12010165
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