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Decoding Alzheimer's AI-Powered Biomarker Analysis for Diagnosis and Monitoring
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Author(s): S. Sivasundarapandian (HKBK College of Engineering, India), Chaithanya Kumar Viralam Ramamurthy (JP Morgan Chase, India), Thrjoram Naresh Reddy Boya (Aurora S Technological Research Institute, India)and Saravanan Matheswaran (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India)
Copyright: 2025
Pages: 24
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.ch002
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Abstract
Alzheimer's disease (AD) necessitates early diagnosis and monitoring for effective management. This study introduces AlzNet, an AI-powered algorithm combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze cerebrospinal fluid (CSF) biomarkers—amyloid-beta (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau181). Leveraging data from 500 participants (200 AD, 150 mild cognitive impairment (MCI), 150 healthy controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), AlzNet demonstrated high accuracy (92.5%), sensitivity (90.3%), specificity (94.7%), and AUC-ROC (0.96) in differentiating between AD, MCI, and controls. Notably, it identified lower Aβ42 and elevated t-tau and p-tau181 levels as significant markers. AlzNet's non-invasive, cost-effective approach and its potential to facilitate early detection and continuous monitoring of AD underscore its clinical utility. Future research will explore its validation across diverse populations and enhance real-time monitoring capabilities.
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