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Dementia and MCI Detection Based on Comprehensive Facial Expression Analysis From Videos During Conversation
Okunishi T, Zheng C, Bouazizi M, Ohtsuki T, Kitazawa M, Horigome T, Kishimoto T.. IEEE journal of biomedical and health informatics, 2025-05-06
Topic: Risk, prevention and care in population studies
The development of a cost-effective digital biomarker for detecting dementia is highly needed. While numerous studies have explored dementia detection through speech and natural language analysis, only a few studies have focused on dementia detection using face video recordings, and more in-depth research is needed. In this paper, we propose a method for detecting dementia and mild cognitive impairment (MCI), a pre-dementia stage, by utilizing four types of facial expression features extracted from recorded videos of participants. These features include Action Units, emotion categories, Valence-Arousal, and face embeddings. From the above features obtained from each video frame, various statistical information was extracted and used as features, and predictions were performed using a decision tree-based model. Our method was evaluated using face video recordings during conversations. The method achieved an area under the receiver operating characteristic curve (AUC) of 0.933 for dementia detection and 0.889 for MCI detection. Statistical analysis of facial expression features revealed that participants with dementia had fewer positive emotions, more negative emotions, and lower valence and arousal than healthy participants. These results indicate that the proposed method could serve as an explainable screening tool for the early detection of dementia and MCI.
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APA 7th
Okunishi, T., Zheng, C., Bouazizi, M., Ohtsuki, T., Kitazawa, M., Horigome, T., et al. (2025). Dementia and MCI Detection Based on Comprehensive Facial Expression Analysis From Videos During Conversation. IEEE journal of biomedical and health informatics. https://doi.org/10.1109/jbhi.2025.3526553
Vancouver
Okunishi T, Zheng C, Bouazizi M, Ohtsuki T, Kitazawa M, Horigome T, et al. Dementia and MCI Detection Based on Comprehensive Facial Expression Analysis From Videos During Conversation. IEEE journal of biomedical and health informatics. 2025. doi:10.1109/jbhi.2025.3526553. PMID: 40031062.
BibTeX
@article{okunishi2025-40031062,
author = {Okunishi, T. and Zheng, C. and Bouazizi, M. and Ohtsuki, T. and Kitazawa, M. and Horigome, T. and Kishimoto, T.},
title = {{Dementia and MCI Detection Based on Comprehensive Facial Expression Analysis From Videos During Conversation}},
journal = {IEEE journal of biomedical and health informatics},
year = {2025},
doi = {10.1109/jbhi.2025.3526553},
pmid = {40031062},
url = {https://doi.org/10.1109/jbhi.2025.3526553}
}
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 names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Statistical analysis of facial expression features revealed that participants with dementia had fewer positive emotions, more negative emotions, and lower valence and arousal than healthy participants.
- These results indicate that the proposed method could serve as an explainable screening tool for the early detection of dementia and MCI.
Study details
- Evidence strengthLow
How this study scored 41/100
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. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 24 of the 58 points we could judge it on, which is 41 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE journal of biomedical and health informatics) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 4 / 5 | 2025 — within 3 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40031062) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 24 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 24 of the 58 points we could judge it on is 41 out of 100. |
| Total | 41 / 100 | — |
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