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Real-World Applications and Case Studies of Deep Generative Models in Alzheimer's Disease Research
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Author(s): Rachna Behl (Manav Rachna International Institute of Research and Studies, India), Indu Kashyap (Manav Rachna International Institute of Research and Studies, India)and Bhanu Dwivedi (Manav Rachna International Institute of Research and Studies, India)
Copyright: 2025
Pages: 18
Source title:
Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers
Source Author(s)/Editor(s): Abhishek Kumar (Chandigarh University, India), S. Rakesh Kumar (GITAM University (Deemed), India), N. Gayathri (GITAM University (Deemed), India), R. Srivel (Adhiparasakthi Engineering College, India)and Dhaya C. (Adhiparasakthi Engineering College, India)
DOI: 10.4018/979-8-3693-6442-0.ch016
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Abstract
Alzheimer's Disease (AD) is an irreversible neurodegenerative condition that causes a gradual decline in cognitive functions. Early AD detection is essential for efficient management. Traditional methods of detection utilizing magnetic resonance imaging (MRI) and other neuropsychological testing have been successful in past decades. However, lack of clinical experts, expensive tests, poor diagnosis require interventions. ML is effective in AD research; however, they fail to handle complex data. Convolution Neural Networks also trigger early prediction of AD but fail due to insufficient data. Deep Generative Models (DGMs) have attracted a lot of attention in recent years. However, the use of Deep Generative Models in Alzheimer research still needs to be validated. Applying DGMs across various disciplines have been promising. This chapter provides an overview of DGMs and their applications in Alzheimer's Research. Real-world applications and case studies are presented by the authors. The chapter discusses the challenges of DGMs and strategies to address challenges.
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