DTI and AI in Alzheimer’s Disease Research: A Commentary

Abstract

Alzheimer's Disease (AD) is a leading cause of cognitive decline in older adults, yet early diagnosis remains a major challenge. Diffusion Tensor Imaging (DTI) has emerged as a promising neuroimaging technique for assessing white matter integrity, a key factor in Alzheimer's progression. This commentary explores the intersection of DTI and Artificial Intelligence (AI), highlighting how advanced machine learning (ML) and deep learning (DL) algorithms are being utilized to process DTI data and improve AD diagnosis. AI models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have demonstrated significant success in predicting AD progression, identifying disease subtypes, and differentiating Alzheimer's from other dementias. Automated Fiber Quantification (AFQ) also plays a critical role in analyzing white matter disruptions associated with AD. This synergistic approach between DTI and AI is advancing early detection efforts and paving the way for more precise and personalized AD diagnoses, ultimately enhancing clinical outcomes and improving our understanding of Alzheimer’s pathology

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