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Review
. 2025 May 2:19:1557177.
doi: 10.3389/fninf.2025.1557177. eCollection 2025.

Advancements in deep learning for early diagnosis of Alzheimer's disease using multimodal neuroimaging: challenges and future directions

Muhammad Liaquat Raza  1   2   3 , Syed Tawassul Hassan  4 , Subia Jamil  5 , Noorulain Hyder  6 , Kinza Batool  7 , Sajidah Walji  5 , Muhammad Khizar Abbas  8
Affiliations
Review

Advancements in deep learning for early diagnosis of Alzheimer's disease using multimodal neuroimaging: challenges and future directions

Muhammad Liaquat Raza et al. Front Neuroinform. .

Abstract

Introduction: Alzheimer's disease is a progressive neurodegenerative disorder challenging early diagnosis and treatment. Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy and predicting disease progression.

Method: This narrative review synthesizes current literature on deep learning applications in Alzheimer's disease diagnosis using multimodal neuroimaging. The review process involved a comprehensive search of relevant databases (PubMed, Embase, Google Scholar and ClinicalTrials.gov), selection of pertinent studies, and critical analysis of findings. We employed a best-evidence approach, prioritizing high-quality studies and identifying consistent patterns across the literature.

Results: Deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models, have shown remarkable potential in analyzing multimodal neuroimaging data. These models can effectively process structural and functional imaging modalities, extracting relevant features and patterns associated with Alzheimer's pathology. Integration of multiple imaging modalities has demonstrated improved diagnostic accuracy compared to single-modality approaches. Deep learning models have also shown promise in predictive modeling, identifying potential biomarkers and forecasting disease progression.

Discussion: While deep learning approaches show great potential, several challenges remain. Data heterogeneity, small sample sizes, and limited generalizability across diverse populations are significant hurdles. The clinical translation of these models requires careful consideration of interpretability, transparency, and ethical implications. The future of AI in neurodiagnostics for Alzheimer's disease looks promising, with potential applications in personalized treatment strategies.

Keywords: Alzheimer’s disease; Disease progression prediction; brain imaging analysis; deep learning; multimodal neuroimaging.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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