Neuroimaging Biomarkers In Hybrid Deep Learning Models; Advancing Precision Medicine For Alzheimer’s Diagnosis

Abstract

A promising strategy for additional creating precision medicine for the diagnosis of Alzheimer's disease (AD) is the consolidation of neuroimaging biomarkers into hybrid deep learning models. The multifaceted design and heterogeneity of AD make early and precise diagnosis troublesome, even with significant advances in neuroimaging techniques. The goal of this research is to make hybrid deep learning models that use multimodal neuroimaging data — joining PET, structural, and pragmatic X-beam scans — to increase diagnostic precision. The model captures minuscule patterns that demonstrate Alzheimer's-related brain alterations by removing irrefutable level features from neuroimaging data using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The philosophy uses extensive datasets of AD patients and sound controls for preprocessing stages such as picture standardization, feature extraction, and model training. The findings show that the hybrid deep learning models beat customary techniques in terms of diagnosis, perceiving significant brain regions most associated with the advancement of disease and accomplishing high sensitivity (up to 90%) and specificity (around 85%). These results surmise that adding neuroimaging biomarkers to deep learning frameworks can significantly chip away at the precision and constancy of Alzheimer's diagnosis, clearing the path for more customized treatment regimens and early intercession techniques. The diagnosis of AD could be upset by this strategy, which provides a speedier and more precise assessment instrument to support clinical judgment.