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Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review
Pellegrini E, Ballerini L, Hernandez MDCV, Chappell FM, González-Castro V, Anblagan D, Danso S, Muñoz-Maniega S et al.. Alzheimer's & dementia (Amsterdam, Netherlands), 2018-08-11
Topic: Risk, prevention and care in population studies
Plain-language summary
Introduction Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. Methods We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries. Results Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1-weighted sequences. Accuracy was highest for differentiating Alzheimer's disease from healthy controls and poor for differentiating healthy controls versus mild cognitive impairment versus Alzheimer's disease or mild cognitive impairment converters versus nonconverters. Accuracy increased using combined data types, but not by data source, sample size, or machine learning method. Discussion Machine learning does not differentiate clinically relevant disease categories yet. More diverse data sets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field.
What this study adds
Evidence grade: Low
This review of 111 studies found that machine learning on brain scans was good at telling Alzheimer's disease apart from healthy aging, but poor at distinguishing healthy controls from mild cognitive impairment or predicting who would convert. The authors conclude that machine learning does not yet differentiate clinically relevant disease categories.
Taken from this record’s own fields, not from reading the paper. The grade is assigned from the abstract’s wording; what that means.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Pellegrini, E., Ballerini, L., Hernandez, M. D. C. V., Chappell, F. M., González-Castro, V., Anblagan, D., et al. (2018). Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. Alzheimer's & dementia (Amsterdam, Netherlands). https://doi.org/10.1016/j.dadm.2018.07.004
Vancouver
Pellegrini E, Ballerini L, Hernandez MDCV, Chappell FM, González-Castro V, Anblagan D, et al. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. Alzheimer's & dementia (Amsterdam, Netherlands). 2018. doi:10.1016/j.dadm.2018.07.004. PMID: 30364671.
BibTeX
@article{pellegrini2018-30364671,
author = {Pellegrini, E. and Ballerini, L. and Hernandez, M. D. C. V. and Chappell, F. M. and González-Castro, V. and Anblagan, D. and Danso, S. and Muñoz-Maniega, S. and others},
title = {{Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review}},
journal = {Alzheimer's \& dementia (Amsterdam, Netherlands)},
year = {2018},
doi = {10.1016/j.dadm.2018.07.004},
pmid = {30364671},
url = {https://doi.org/10.1016/j.dadm.2018.07.004}
}
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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
Key findings
- Results Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1-weighted sequences.
- Accuracy was highest for differentiating Alzheimer's disease from healthy controls and poor for differentiating healthy controls versus mild cognitive impairment versus Alzheimer's disease or mild cognitive impairment converters versus nonconverters.
Why it matters
This review of 111 studies found that machine learning on brain scans was good at telling Alzheimer's disease apart from healthy aging, but poor at distinguishing healthy controls from mild cognitive impairment or predicting who would convert. The authors conclude that machine learning does not yet differentiate clinically relevant disease categories.
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Study details
- Evidence strengthLow
How this study scored 51/100
Published in a leading specialty journal. Scored zero for study design, sample size, effect reported, studied in people and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Alzheimer's & dementia (Amsterdam, Netherlands)) |
| Journal's citation record | 1 / 4 | Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring: journal h-index 86 (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 | 2 / 5 | 2018 — within 10 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) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 296 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 29.6 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 3 / 6 | Cited 5.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 13 other studies in this library |
| Still being cited | 3 / 4 | Cited 87 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Joanna Marguerite Wardlaw: h-index 150 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Edinburgh: institution h-index 1,378 (OpenAlex) |
| Multicenter collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30364671) |
| Free to read | 2 / 2 | Free to read |
| Total | 51 / 100 | — |
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Read the original
- DOI: 10.1016/j.dadm.2018.07.004
- PubMed: 30364671
- Original source →
- Free full text: Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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