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Machine Learning Based Autism Detect...

Katuwal, Gajendra Jung.

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Machine Learning Based Autism Detection Using Brain Imaging.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Based Autism Detection Using Brain Imaging./
作者:	Katuwal, Gajendra Jung.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	177 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10284504
ISBN:	9781369795189

Machine Learning Based Autism Detection Using Brain Imaging.
Katuwal, Gajendra Jung.

Machine Learning Based Autism Detection Using Brain Imaging. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 177 p.

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--Rochester Institute of Technology, 2017.

Autism Spectrum Disorder (ASD) is a group of heterogeneous developmental disabilities that manifest in early childhood. Currently, ASD is primarily diagnosed by assessing the behavioral and intellectual abilities of a child. This behavioral diagnosis can be subjective, time consuming, inconclusive, does not provide insight on the underlying etiology, and is not suitable for early detection. Diagnosis based on brain magnetic resonance imaging (MRI)---a widely used non- invasive tool---can be objective, can help understand the brain alterations in ASD, and can be suitable for early diagnosis. However, the brain morphological findings in ASD from MRI studies have been inconsistent. Moreover, there has been limited success in machine learning based ASD detection using MRI derived brain features. In this thesis, we begin by demonstrating that the low success in ASD detection and the inconsistent findings are likely attributable to the heterogeneity of brain alterations in ASD. We then show that ASD detection can be significantly improved by mitigating the heterogeneity with the help of behavioral and demographics information. Here we demonstrate that finding brain markers in well-defined sub-groups of ASD is easier and more insightful than identifying markers across the whole spectrum. Finally, our study focused on brain MRI of a pediatric cohort (3 to 4 years) and achieved a high classification success (AUC of 95%). Results of this study indicate three main alterations in early ASD brains: 1) abnormally large ventricles, 2) highly folded cortices, and 3) low image intensity in white matter regions suggesting myelination deficits indicative of decreased structural connectivity. Results of this thesis demonstrate that the meaningful brain markers of ASD can be extracted by applying machine learning techniques on brain MRI data. This data-driven technique can be a powerful tool for early detection and understanding brain anatomical underpinnings of ASD.

ISBN: 9781369795189Subjects--Topical Terms:

553671
Bioinformatics.

Machine Learning Based Autism Detection Using Brain Imaging.
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