Deep learning for Alzheimer's disease: advances in classification, segmentation, subtyping, and explainability.

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Abstract

Alzheimer's disease (AD) poses urgent significant challenges for early detection and personalized prognostication. Deep learning (DL) is now regarded as a pivotal technology for extracting subtle imaging and non-imaging biomarkers; yet translating these advances into clinical practice demands a coherent framework. In this review, we first survey input modalities from structural and functional MRI to PET, genetic profiles, and cognitive tests and the key public cohorts that supply multimodal data. We then categorize DL architectures into three complementary pillars: (1) end-to-end classification networks for direct diagnosis; (2) multimodal fusion strategies that integrate heterogeneous biomarkers; and (3) automated segmentation pipelines for precise anatomical delineation. We also examine subtyping algorithms that uncover latent AD phenotypes via clustering and decision-tree models. In order to fill the gap between high-performance DL and real-world adoption, we detail explainable AI methods that render model decisions transparent, and we review performance benchmarks including accuracy, sensitivity/specificity, Dice and Jaccard indices to contextualize efficacy. Finally, we discuss clinical translation, covering prospective validation, workflow integration, and regulatory/privacy considerations, before outlining challenges and future directions such as data heterogeneity, interpretability-accuracy trade-offs, early/preclinical detection, and federated learning. Our roadmap highlights the interdisciplinary collaborations and technical innovations needed to deliver robust, trustworthy, and scalable DL-based tools for Alzheimer's care.

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