Home › Research › Digital health tools
Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review
Frizzell TO, Glashutter M, Liu CC, Zeng A, Pan D, Hajra SG, D'Arcy RCN, Song X.. Ageing research reviews, 2022-03-28
Topic: Amyloid, tau and blood-test diagnosis
Introduction Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis. Methods A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria. Results 97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods. Discussion The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.
Key findings
- Introduction Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI).
- Results 97 relevant studies were included in the review.
Why it matters
This review of 97 studies found that artificial intelligence applied to brain MRI was typically focused on classifying Alzheimer's disease, mild cognitive impairment, and normal aging or predicting conversion from mild cognitive impairment to Alzheimer's disease, with the best performance from deep learning convolution neural networks. It concerns imaging analysis and diagnosis, not treating, curing, or preventing Alzheimer's disease.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Study designsystematic review
- Evidence strengthHigh
How this study scored 62/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Ageing research reviews) |
| Journal's citation record | 2 / 4 | Ageing Research Reviews: journal h-index 225 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A systematic review of all the studies on the question |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 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 | A review of other studies — randomisation is not credited here |
| Times cited | 5 / 7 | Cited 141 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 23.5 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 3 / 6 | Cited 6.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 3 / 4 | Cited 110 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Xiaowei Song: h-index 39 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Simon Fraser University: institution h-index 657 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 9 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35358720) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 62 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
May need a subscription OpenAlex found no free copy of the full text (checked 6 Feb 1020), so the publisher may charge for it. The PubMed abstract is free.
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. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Frizzell, T. O., Glashutter, M., Liu, C. C., Zeng, A., Pan, D., Hajra, S. G., et al. (2022). Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review. Ageing research reviews. https://doi.org/10.1016/j.arr.2022.101614
Vancouver
Frizzell TO, Glashutter M, Liu CC, Zeng A, Pan D, Hajra SG, et al. Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review. Ageing research reviews. 2022. doi:10.1016/j.arr.2022.101614. PMID: 35358720.
BibTeX
@article{frizzell2022-35358720,
author = {Frizzell, T. O. and Glashutter, M. and Liu, C. C. and Zeng, A. and Pan, D. and Hajra, S. G. and D'Arcy, R. C. N. and Song, X.},
title = {{Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review}},
journal = {Ageing research reviews},
year = {2022},
doi = {10.1016/j.arr.2022.101614},
pmid = {35358720},
url = {https://doi.org/10.1016/j.arr.2022.101614}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 26.
Later studies that cite this one
6 studies in the alzhub library list this one in their references.
Related studies in Digital health tools
Also tagged “classification”
835 studies in the library carry the topic tag “classification”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 835 tagged studies.