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Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review
Ansart M, Epelbaum S, Bassignana G, Bône A, Bottani S, Cattai T, Couronné R, Faouzi J et al.. Medical image analysis, 2020-10-06
Topic: Risk, prevention and care in population studies
We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we reported the used data set, the feature types, the algorithm type, performance and potential methodological issues. The impact of these characteristics on the performance was evaluated using a multivariate mixed effect linear regressions. We found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalography variables significantly improved predictive performance compared to not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect. The good performance of cognitive assessments questions the wide use of imaging for predicting the progression to AD and advocates for exploring further fine domain-specific cognitive assessments. We also identified several methodological issues, including the absence of a test set, or its use for feature selection or parameter tuning in nearly a fourth of the papers. Other issues, found in 15% of the studies, cast doubts on the relevance of the method to clinical practice. We also highlight that short-term predictions are likely not to be better than predicting that subjects stay stable over time. These issues highlight the importance of adhering to good practices for the use of machine learning as a decision support system for the clinical practice.
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APA 7th
Ansart, M., Epelbaum, S., Bassignana, G., Bône, A., Bottani, S., Cattai, T., et al. (2021). Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Medical image analysis. https://doi.org/10.1016/j.media.2020.101848
Vancouver
Ansart M, Epelbaum S, Bassignana G, Bône A, Bottani S, Cattai T, et al. Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Medical image analysis. 2021. doi:10.1016/j.media.2020.101848. PMID: 33091740.
BibTeX
@article{ansart2021-33091740,
author = {Ansart, M. and Epelbaum, S. and Bassignana, G. and Bône, A. and Bottani, S. and Cattai, T. and Couronné, R. and Faouzi, J. and others},
title = {{Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review}},
journal = {Medical image analysis},
year = {2021},
doi = {10.1016/j.media.2020.101848},
pmid = {33091740},
url = {https://doi.org/10.1016/j.media.2020.101848}
}
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
- We found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalography variables significantly improved predictive performance compared to not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect.
Why it matters
This review of 172 articles found that using cognitive, fluorodeoxyglucose-positron emission tomography, or potentially electroencephalography and magnetoencephalography variables significantly improved prediction of progression from mild cognitive impairment to Alzheimer's dementia, whereas including T1 magnetic resonance imaging did not show a significant effect. It also identified methodological issues in many papers and concerns prediction, not treating, curing, or preventing Alzheimer's disease.
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Study details
- Study designsystematic review
- Participants172
- Evidence strengthHigh
How this study scored 70/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for effect reported because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Medical image analysis) |
| Journal's citation record | 2 / 4 | Medical Image Analysis: journal h-index 221 (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 | 2 / 4 | 172 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | A review of other studies — randomisation is not credited here |
| Times cited | 5 / 7 | Cited 116 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 16.6 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 4 / 6 | Cited 8.6× 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 62 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Stanley Durrleman: h-index 38 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Centre National de la Recherche Scientifique: institution h-index 2,511 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 9 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33091740) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 70 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.media.2020.101848
- PubMed: 33091740
- Original source →
- Free full text: Medical Image Analysis →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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