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Identifying incipient dementia individuals using machine learning and amyloid imaging
Mathotaarachchi S, Pascoal TA, Shin M, Benedet AL, Kang MS, Beaudry T, Fonov VS, Gauthier S et al.. Neurobiology of aging, 2017-07-11
Topic: Amyloid, tau and blood-test diagnosis
Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the core pathologic feature of Alzheimer's disease, biomarkers of neuronal degeneration are the only ones believed to provide satisfactory predictions of clinical progression within short time frames. Here, we propose a machine learning-based probabilistic method designed to assess the progression to dementia within 24 months, based on the regional information from a single amyloid positron emission tomography scan. Importantly, the proposed method was designed to overcome the inherent adverse imbalance proportions between stable and progressive mild cognitive impairment individuals within a short observation period. The novel algorithm obtained an accuracy of 84% and an under-receiver operating characteristic curve of 0.91, outperforming the existing algorithms using the same biomarker measures and previous studies using multiple biomarker modalities. With its high accuracy, this algorithm has immediate applications for population enrichment in clinical trials designed to test disease-modifying therapies aiming to mitigate the progression to Alzheimer's disease dementia.
Key findings
- Although amyloid-β protein is the core pathologic feature of Alzheimer's disease, biomarkers of neuronal degeneration are the only ones believed to provide satisfactory predictions of clinical progression within short time frames.
- Here, we propose a machine learning-based probabilistic method designed to assess the progression to dementia within 24 months, based on the regional information from a single amyloid positron emission tomography scan.
- Importantly, the proposed method was designed to overcome the inherent adverse imbalance proportions between stable and progressive mild cognitive impairment individuals within a short observation period.
- The novel algorithm obtained an accuracy of 84% and an under-receiver operating characteristic curve of 0.91, outperforming the existing algorithms using the same biomarker measures and previous studies using multiple biomarker modalities.
- With its high accuracy, this algorithm has immediate applications for population enrichment in clinical trials designed to test disease-modifying therapies aiming to mitigate the progression to Alzheimer's disease dementia.
Why it matters
This study developed a machine learning method using a single amyloid PET scan to assess progression to dementia within 24 months, with 84% accuracy. The authors say it has immediate applications for population enrichment in clinical trials of disease-modifying therapies, but it does not itself test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 47/100
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 (Neurobiology of aging) |
| Journal's citation record | 3 / 4 | Neurobiology of Aging: journal h-index 263 (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 | 2017 — 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) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 120 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 10.9 citations a year over 11 year(s) since 2017 |
| 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 | 3 / 6 | Cited by 10 other studies in this library |
| Still being cited | 2 / 4 | Cited 24 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Pedro Rosa‐Neto: h-index 89 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | McGill University: institution h-index 1,373 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28756942) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 47 / 100 | — |
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Read the original
- DOI: 10.1016/j.neurobiolaging.2017.06.027
- PubMed: 28756942
- Original source →
- Free full text: Neurobiology of Aging →
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. 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.
APA 7th
Mathotaarachchi, S., Pascoal, T. A., Shin, M., Benedet, A. L., Kang, M. S., Beaudry, T., et al. (2017). Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiology of aging. https://doi.org/10.1016/j.neurobiolaging.2017.06.027
Vancouver
Mathotaarachchi S, Pascoal TA, Shin M, Benedet AL, Kang MS, Beaudry T, et al. Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiology of aging. 2017. doi:10.1016/j.neurobiolaging.2017.06.027. PMID: 28756942.
BibTeX
@article{mathotaarachchi2017-28756942,
author = {Mathotaarachchi, S. and Pascoal, T. A. and Shin, M. and Benedet, A. L. and Kang, M. S. and Beaudry, T. and Fonov, V. S. and Gauthier, S. and others},
title = {{Identifying incipient dementia individuals using machine learning and amyloid imaging}},
journal = {Neurobiology of aging},
year = {2017},
doi = {10.1016/j.neurobiolaging.2017.06.027},
pmid = {28756942},
url = {https://doi.org/10.1016/j.neurobiolaging.2017.06.027}
}
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