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Artificial Intelligence for Predicting Progression and Personalizing Healthcare to Alzheimer's Disease Patients

Book Chapter

Authors: Imreena Ali; Vijaya Kittu Manda

DOI: https://doi.org/10.4018/979-8-3693-7858-8.ch006

Book Name: AI-Driven Personalized Healthcare Solutions

ISBN: ISBN13: 9798369378588|ISBN13 Softcover: 9798369378595|EISBN13: 9798369378601

Publisher: IGI Global, USA

Editor(s): Houssem Chemingui and Meriam Lamloumi

Book Chapter Number: 6

Publication Date: March 6, 2025

Pages: 155 - 190

Book Chapter Link on Publisher Website

Book Link on Publisher Website

Abstract: This chapter explains the use of Deep Learning Models from Artificial Intelligence (AI) that take Structural and Functional Magnetic Resonance Imaging (S/FMRI) data to classify Alzheimer's disease (AD) progression stages. Early and accurate diagnosis of AD is necessary for timely intervention, treatment planning, and providing personalized healthcare. Current limitations in diagnostic methods necessitate using AI methods such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to extract features from MRI data and develop models for predicting Mild Cognitive Impairment (MCI), AD, and Dementia. Initial results from a case study that applied the methodology demonstrated improved classification accuracy over traditional methods in accurately classifying disease stages and developing patient care. With more refinement as AI technologies progress, these computational approaches can drastically and positively change patient care. Healthcare professionals benefit from this chapter by understanding how AI can be implemented to deal with neurodegenerative diseases.

Keywords: Alzheimers Disease, Deep Learning, Disease Progression, Neurodegenerative Diseases, Multimodal Imaging, Semantic Segmentation, Disease Stage, Dementia, MRI Data, SMRI, FMRI, MRI Dataset, Biomarkers, Mild Cognitive Impairment

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