A Comparative Analysis of Multi-modal Imaging Approaches for Early Detection of Alzheimer's Disease

Abstract

Alzheimer's disease (AD) poses a growing global health challenge, necessitating early diagnostic tools to improve intervention strategies. This study conducts a comparative analysis of three multi-modal imaging techniques—MRI, PET, and SPECT—on a cohort of 300 subjects at various stages of AD. Utilizing advanced machine learning algorithms, including Random Forest and Support Vector Machines, we assessed diagnostic accuracy and identified key predictive biomarkers. Results indicated that simultaneous application of MRI and PET yielded a sensitivity of 93% with a specificity of 90%, outperforming individual modalities. This research underscores the critical role of multi-modal imaging in mitigating the socio-economic burden of AD, providing a framework for future diagnostic innovations.