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Prognostic Applications for Alzheime...

Bhagwat, Nikhil.

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Prognostic Applications for Alzheimer's Disease Using Magnetic Resonance Imaging and Machine-Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prognostic Applications for Alzheimer's Disease Using Magnetic Resonance Imaging and Machine-Learning./
Author:	Bhagwat, Nikhil.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	199 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
Contained By:	Dissertations Abstracts International80-06B.
Subject:	Neurosciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10936394
ISBN:	9780438681095

Prognostic Applications for Alzheimer's Disease Using Magnetic Resonance Imaging and Machine-Learning.
Bhagwat, Nikhil.

Prognostic Applications for Alzheimer's Disease Using Magnetic Resonance Imaging and Machine-Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 199 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2018.

This item must not be sold to any third party vendors.

Alzheimer's disease (AD), the most common form of dementia, is a neurodegenerative disorder that leads to cognitive deficits, particularly in the memory domain. Recent advances in magnetic resonance (MR) imaging techniques and computational tools, such as machine-learning (ML), provide promising opportunity for prognostic applications in AD. Imaging biomarkers can improve our understanding of etiology and progression of the disease, as well as assist clinicians in decision-making pertaining to patient monitoring, intervention, and treatment selection. The overarching goal of this thesis is to develop several computational methods that facilitate the use of MR imaging data in translational applications to improve personalized patient care. The work in this thesis is divided into three projects. The first project aims to improve MR image segmentation - a commonly used MR image processing step to delineate anatomical structures used in a multitude of downstream quantitative analyses. The goal of the second project is to leverage MR-based anatomical features towards subject-level clinical severity prediction. Methodologically, the work provides a novel ML framework for high-dimensional, multimodal analysis customized for MR imaging data. The third project extends the subject-level prediction towards longitudinal prognosis with several practical considerations. The methodological contributions involve modeling and prediction of clinical trajectories from longitudinal MR and clinical measures using ML approaches. The work addresses many challenges faced in a clinical setting, such as missing data points, and provides a powerful framework for early detection and accurate prognosis of at-risk AD patients via continuous monitoring. The comprehensive validation of the methods presented in this work with multiple datasets and studies demonstrates the utility of MR images towards AD prognosis. The tools proposed complement existing clinical workflow and can be leveraged in conjunction with current clinical assessments. With the increasing availability of large-scale datasets, further improvements and validations can be made to adopt this work for individual intervention and prognosis, as well as for improving recruitment strategies for the clinical trials in AD.

ISBN: 9780438681095Subjects--Topical Terms:

588700
Neurosciences.

Prognostic Applications for Alzheimer's Disease Using Magnetic Resonance Imaging and Machine-Learning.
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520 $a Alzheimer's disease (AD), the most common form of dementia, is a neurodegenerative disorder that leads to cognitive deficits, particularly in the memory domain. Recent advances in magnetic resonance (MR) imaging techniques and computational tools, such as machine-learning (ML), provide promising opportunity for prognostic applications in AD. Imaging biomarkers can improve our understanding of etiology and progression of the disease, as well as assist clinicians in decision-making pertaining to patient monitoring, intervention, and treatment selection. The overarching goal of this thesis is to develop several computational methods that facilitate the use of MR imaging data in translational applications to improve personalized patient care. The work in this thesis is divided into three projects. The first project aims to improve MR image segmentation - a commonly used MR image processing step to delineate anatomical structures used in a multitude of downstream quantitative analyses. The goal of the second project is to leverage MR-based anatomical features towards subject-level clinical severity prediction. Methodologically, the work provides a novel ML framework for high-dimensional, multimodal analysis customized for MR imaging data. The third project extends the subject-level prediction towards longitudinal prognosis with several practical considerations. The methodological contributions involve modeling and prediction of clinical trajectories from longitudinal MR and clinical measures using ML approaches. The work addresses many challenges faced in a clinical setting, such as missing data points, and provides a powerful framework for early detection and accurate prognosis of at-risk AD patients via continuous monitoring. The comprehensive validation of the methods presented in this work with multiple datasets and studies demonstrates the utility of MR images towards AD prognosis. The tools proposed complement existing clinical workflow and can be leveraged in conjunction with current clinical assessments. With the increasing availability of large-scale datasets, further improvements and validations can be made to adopt this work for individual intervention and prognosis, as well as for improving recruitment strategies for the clinical trials in AD.
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