Application of Multimodal Machine Learning in Early Alzheimer's Disease Detection

This project proposes a multimodal machine learning framework integrating MRI images, clinical data, blood biomarkers, and genetic features. Through feature fusion and deep learning methods, it aims to break through the limitations of traditional single-modal diagnosis and improve the diagnostic accuracy of early Alzheimer's Disease (AD).

This project proposes a multimodal machine learning framework that integrates MRI images, clinical data, blood biomarkers, and genetic features, aiming to improve the diagnostic accuracy of early Alzheimer's disease through feature fusion and deep learning methods.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the main cause of dementia in the elderly worldwide. With the growing aging of the population, the incidence of AD continues to rise, placing a heavy burden on healthcare systems, society, and families.

Early diagnosis is key to delaying disease progression and improving patients' quality of life. However, traditional single-modal diagnostic methods face many limitations: although MRI images can show brain structural changes, early lesions are often not obvious; cognitive assessment scales (such as MMSE, CDR) are highly subjective; blood tests are convenient, but the identification of specific biomarkers is still under research.

The multimodal machine learning system developed by Junaidkalam is designed to break through these limitations by integrating multi-source heterogeneous data to build a more comprehensive disease characterization model.

The project integrates four core data modalities:

MRI Neuroimaging: Uses structural brain MRI scans. Through image preprocessing (normalization, resampling, enhancement) and feature extraction (based on CNN or hand-designed features), it captures structural indicators such as hippocampal atrophy and cortical thickness changes. These changes often appear earlier than clinical symptoms.

Clinical Assessment Data: Includes standardized cognitive test scores such as the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR), as well as demographic characteristics (age, gender, education level) and medical history information. These data provide direct measurements of cognitive function.

Blood Biomarkers: Covers proteins and biochemical indicators related to AD pathology, such as β-amyloid (Aβ) and tau protein. In recent years, blood testing has become an important tool for AD screening due to its minimally invasive nature and accessibility.

Genetic Data: Includes gene expression profiles or genotype features related to AD risk, such as the APOE ε4 allele status. Genetic factors play an important role in the onset of AD, and polygenic risk scores (PRS) can supplement information from other modalities.