Implications for Improving Alzheimer's Disease Diagnosis Using Multiple Modalities

This study provides a comprehensive method for staging and analysing Alzheimer's disease (AD) by combining multiple data modalities. Patients are categorized as Cognitive Normal (CN), Mild Cognitive Impairment (MCI), or AD using deep learning (DL), which analyses imaging, genetic, and clinical data. 3D-CNNs with stacked denoising autoencoders extract important features. This study showed that deep learning (DL) models outperform shallow models using the Alzheimer's Disease Neuroimaging Dataset (ADNI). Precision, memory, F1-scores, and accuracy were all enhanced by multi-modality data integration, which also highlighted RAVLT, amygdala, and hippocampal aspects.