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Research progress in predicting the conversion from mild cognitive impairment to Alzheimer’s disease via multimodal MRI and artificial intelligence
Min Ai, Yü Liu, Dan Liu, Chengxi Yan, Xia Wang, Xun Chen. Frontiers in Neurology, 2025-06-02
Topic: Amyloid, tau and blood-test diagnosis
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.
Key findings
- 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.
Why it matters
This review describes how multimodal MRI combined with AI may help predict conversion from mild cognitive impairment to Alzheimer's disease. It is a review of prediction research and reports no treatment, cure, or prevention.
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Study details
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in Neurology) |
| Journal's citation record | 2 / 4 | Frontiers in Neurology: journal h-index 162 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 15 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 9.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 15 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Xun Chen: h-index 65 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Chongqing University: institution h-index 493 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40529431) |
| Free to read | 2 / 2 | Free to read |
| Total | 43 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fneur.2025.1596632
- PubMed: 40529431
- Original source →
- Free full text: Frontiers in Neurology →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Ai, M., Liu, Y., Liu, D., Yan, C., Wang, X., & Chen, X. (2025). Research progress in predicting the conversion from mild cognitive impairment to Alzheimer’s disease via multimodal MRI and artificial intelligence. Frontiers in Neurology. https://doi.org/10.3389/fneur.2025.1596632
Vancouver
Ai M, Liu Y, Liu D, Yan C, Wang X, Chen X. Research progress in predicting the conversion from mild cognitive impairment to Alzheimer’s disease via multimodal MRI and artificial intelligence. Frontiers in Neurology. 2025. doi:10.3389/fneur.2025.1596632. PMID: 40529431.
BibTeX
@article{ai2025-40529431,
author = {Ai, Min and Liu, Yü and Liu, Dan and Yan, Chengxi and Wang, Xia and Chen, Xun},
title = {{Research progress in predicting the conversion from mild cognitive impairment to Alzheimer’s disease via multimodal MRI and artificial intelligence}},
journal = {Frontiers in Neurology},
year = {2025},
doi = {10.3389/fneur.2025.1596632},
pmid = {40529431},
url = {https://doi.org/10.3389/fneur.2025.1596632}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.