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Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach
Amini S, Hao B, Zhang L, Song M, Gupta A, Karjadi C, Kolachalama VB, Au R et al.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2022-07-07
Topic: Risk, prevention and care in population studies
Plain-language summary
Introduction Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia. Methods A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics. Results Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. Discussion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.
What this study adds
Evidence grade: Low
The record lists 1,084 participants. This study developed a natural language processing approach to detect mild cognitive impairment and dementia from voice recordings of neuropsychological tests. Reported effect size: 92.6%; 88.0%; 74.4%.
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
Amini, S., Hao, B., Zhang, L., Song, M., Gupta, A., Karjadi, C., et al. (2023). Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.12721
Vancouver
Amini S, Hao B, Zhang L, Song M, Gupta A, Karjadi C, et al. Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023. doi:10.1002/alz.12721. PMID: 35796399.
BibTeX
@article{amini2023-35796399,
author = {Amini, S. and Hao, B. and Zhang, L. and Song, M. and Gupta, A. and Karjadi, C. and Kolachalama, V. B. and Au, R. and others},
title = {{Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2023},
doi = {10.1002/alz.12721},
pmid = {35796399},
url = {https://doi.org/10.1002/alz.12721}
}
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 Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively.
Why it matters
This study developed a natural language processing approach to detect mild cognitive impairment and dementia from voice recordings of neuropsychological tests. It achieved high accuracy in differentiating normal cognition from dementia, but it is a screening tool, not a treatment or cure.
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Study details
- Participants1,084
- Effect size92.6%; 88.0%; 74.4%
- Evidence strengthLow
How this study scored 55/100
Published in a leading specialty journal. Scored zero for study design 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 : the journal of the Alzheimer's Association) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (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 | 2023 — within 6 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,084 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) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 74 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.8 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 4 / 6 | Cited 8.9× 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 60 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Ioannis Ch. Paschalidis: h-index 41 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Boston University: institution h-index 1,456 (OpenAlex) |
| Multicenter collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35796399) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 55 / 100 | — |
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