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Screening of Alzheimer's disease by facial complexion using artificial intelligence
Umeda-Kameyama Y, Kameyama M, Tanaka T, Son BK, Kojima T, Fukasawa M, Iizuka T, Ogawa S et al.. Aging, 2021-01-25
Topic: Amyloid, tau and blood-test diagnosis
Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia is yet to be discovered. This study aimed to examine whether artificial intelligence (AI) could distinguish between the faces of people with cognitive impairment and those without dementia.121 patients with cognitive impairment and 117 cognitively sound participants were recruited for the study. 5 deep learning models with 2 optimizers were tested. The binary differentiation of dementia / non-dementia facial image was expressed as a "Face AI score". Xception with Adam was the model that showed the best performance. Overall sensitivity, specificity, and accuracy by the Xception AI system and AUC of the ROC curve were 87.31%, 94.57%, 92.56%, and 0.9717, respectively. Close and significant correlations were found between Face AI score and MMSE ( r = -0.599, p r = 0.321, p p < 0.0001). The study showed that deep learning programs such as Xception have the ability to differentiate the faces of patients with mild dementia from that of patients without dementia, paving the way for future studies into the development of a facial biomarker for dementia.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Umeda-Kameyama, Y., Kameyama, M., Tanaka, T., Son, B. K., Kojima, T., Fukasawa, M., et al. (2021). Screening of Alzheimer's disease by facial complexion using artificial intelligence. Aging. https://doi.org/10.18632/aging.202545
Vancouver
Umeda-Kameyama Y, Kameyama M, Tanaka T, Son BK, Kojima T, Fukasawa M, et al. Screening of Alzheimer's disease by facial complexion using artificial intelligence. Aging. 2021. doi:10.18632/aging.202545. PMID: 33495415.
BibTeX
@article{umedakameyama2021-33495415,
author = {Umeda-Kameyama, Y. and Kameyama, M. and Tanaka, T. and Son, B. K. and Kojima, T. and Fukasawa, M. and Iizuka, T. and Ogawa, S. and others},
title = {{Screening of Alzheimer's disease by facial complexion using artificial intelligence}},
journal = {Aging},
year = {2021},
doi = {10.18632/aging.202545},
pmid = {33495415},
url = {https://doi.org/10.18632/aging.202545}
}
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Key findings
- Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia is yet to be discovered.
- Overall sensitivity, specificity, and accuracy by the Xception AI system and AUC of the ROC curve were 87.31%, 94.57%, 92.56%, and 0.9717, respectively.
- Close and significant correlations were found between Face AI score and MMSE ( r = -0.599, p r = 0.321, p p < 0.0001).
- The study showed that deep learning programs such as Xception have the ability to differentiate the faces of patients with mild dementia from that of patients without dementia, paving the way for future studies into the development of a facial biomarker for dementia.
Why it matters
A deep learning system distinguished faces of patients with mild dementia from those without dementia with high accuracy in this sample. It is a screening study only and reports no treatment, cure, or prevention.
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Study details
- Participants121
- Effect size87.31%; 94.57%; 92.56%
- Evidence strengthLow
How this study scored 54/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Aging) |
| Journal's citation record | 2 / 4 | Aging: journal h-index 162 (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 | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 2 / 4 | 121 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 73 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 10.4 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 3 / 6 | Cited 6.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 50 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Masahiro Akishita: h-index 73 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | The University of Tokyo: institution h-index 1,463 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33495415) |
| Free to read | 2 / 2 | Free to read |
| Total | 54 / 100 | — |
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