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Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models
Amini S, Hao B, Yang J, Karjadi C, Kolachalama VB, Au R, Paschalidis IC.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2024-06-25
Topic: Amyloid, tau and blood-test diagnosis
Introduction Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. Methods We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. Results Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. Discussion The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. Highlights Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.
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APA 7th
Amini, S., Hao, B., Yang, J., Karjadi, C., Kolachalama, V. B., Au, R., et al. (2024). Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.13886
Vancouver
Amini S, Hao B, Yang J, Karjadi C, Kolachalama VB, Au R, et al. Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024. doi:10.1002/alz.13886. PMID: 38924662.
BibTeX
@article{amini2024-38924662,
author = {Amini, S. and Hao, B. and Yang, J. and Karjadi, C. and Kolachalama, V. B. and Au, R. and Paschalidis, I. C.},
title = {{Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2024},
doi = {10.1002/alz.13886},
pmid = {38924662},
url = {https://doi.org/10.1002/alz.13886}
}
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Key findings
- Results Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years.
Why it matters
This study used speech recordings and machine learning to predict which people with mild cognitive impairment would develop Alzheimer's disease within 6 years, with 78.5% accuracy and 81.1% sensitivity. It suggests a possible low-cost, easy-to-administer screening tool, but it is a prediction method, not a treatment or cure.
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Study details
- Participants166
- Effect size78.5%; 81.1%
- Evidence strengthLow
How this study scored 57/100
Published in a leading specialty journal. Scored zero for study design and randomised / 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 | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 166 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 71 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 17.8 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 4 / 6 | Cited 19.3× 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 71 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) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38924662) |
| Free to read | 2 / 2 | Free to read |
| Total | 57 / 100 | — |
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