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Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review
Grueso S, Viejo-Sobera R.. Alzheimer's research & therapy, 2021-09-28
Topic: Amyloid, tau and blood-test diagnosis
Background An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. Methods We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. Results Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. Conclusions Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
Key findings
- Results Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography.
- Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique.
- Conclusions Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
Why it matters
This systematic review examined machine learning methods applied to neuroimaging data to predict whether people with mild cognitive impairment would develop Alzheimer's disease dementia. It reports that results are promising as a support tool for clinicians, but it is about prediction, not treating or curing the disease.
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Study details
- Study designsystematic review
- Effect size75.4%; 78.5%
- Evidence strengthHigh
How this study scored 65/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size and studied in people because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer's research & therapy) |
| Journal's citation record | 1 / 4 | Alzheimer s Research & Therapy: journal h-index 146 (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 | 2021 — 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 299 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 42.7 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 4 / 6 | Cited 9.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 14 other studies in this library |
| Still being cited | 4 / 4 | Cited 217 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Raquel Viejo-Sobera: h-index 10 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Universitat Oberta de Catalunya: institution h-index 197 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 34583745) |
| Free to read | 2 / 2 | Free to read |
| Total | 65 / 100 | — |
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Read the original
- DOI: 10.1186/s13195-021-00900-w
- PubMed: 34583745
- Original source →
- Free full text: Alzheimer s Research & Therapy →
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
Grueso, S., & Viejo-Sobera, R. (2021). Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimer's research & therapy. https://doi.org/10.1186/s13195-021-00900-w
Vancouver
Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimer's research & therapy. 2021. doi:10.1186/s13195-021-00900-w. PMID: 34583745.
BibTeX
@article{grueso2021-34583745,
author = {Grueso, S. and Viejo-Sobera, R.},
title = {{Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review}},
journal = {Alzheimer's research \& therapy},
year = {2021},
doi = {10.1186/s13195-021-00900-w},
pmid = {34583745},
url = {https://doi.org/10.1186/s13195-021-00900-w}
}
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