ReviewFrontiers in neurology2025
Research progress in predicting the conversion from mild cognitive impairment to Alzheimer's disease via multimodal MRI and artificial intelligence.
Review in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.Scientific reports · 2026Article
- From Structure to Function and Back Again: A GAN-Guided Diffusion Framework for Generating Clinically Meaningful Multimodal Neuroimaging Data.Research square · 2026Article
- Multitechnological integration advances musculoskeletal regeneration: synergistic progress of organoids, 3D/4D bioprinting, single-cell omics and artificial intelligence.Frontiers in bioengineering and biotechnology · 2026Review
- Striatal functional connectivity alterations in mild cognitive impairment subtypes defined by CSF A/T biomarkers.Frontiers in aging neuroscience · 2026Article
- Integrating anatomical priors and clinical semantics for MRI-based diagnosis and care support in Alzheimer's disease.Frontiers in neuroscience · 2026Article
- AI agents in Alzheimer's disease management: challenges and future directions.Frontiers in aging neuroscience · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Predicting the transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance for dementia prevention and improving patient prognosis. Multimodal magnetic resonance imaging (MRI) techniques (including structural MRI, functional MRI, and cerebral perfusion MRI) can yield information on the morphology, structure, and function of the brain from multiple dimensions, providing a key basis for revealing the pathophysiological mechanisms during the conversion from MCI to AD. Artificial intelligence (AI) methods based on deep learning and machine learning, with their powerful data processing and pattern recognition capabilities, have shown great potential in mining the features of multimodal MRI data and constructing prediction models for MCI conversion. Therefore, this paper systematically reviews the research progress of multimodal MRI techniques in capturing brain changes related to MCI conversion, as well as the practical experience of AI algorithms in constructing efficient prediction models, analyses the current technical challenges faced by the research, and discusses future directions, with the goal of providing a scientific reference for the early and accurate prediction of MCI conversion and the formulation of intervention strategies.
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Registered trials
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