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Multi-modal data analysis for early detection of alzheimer’s disease and related dementias
Liming Wang, Jim Glass, Lampros C. Kourtis, Rhoda Au. The Journal of Prevention of Alzheimer s Disease, 2025-12-01
Topic: Amyloid, tau and blood-test diagnosis
Until recently, accurate early detection of clinical symptoms associated with Alzheimer's disease (AD) and related dementias (ADRD) has been difficult. Digital technologies have created new opportunities to capture cognitive and other AD/ADRD related behaviors with greater sensitivity and specificity. Speech captured through digital recordings has shown recent promise at feasible levels of scalability because of the widespread penetration of smartphones. One such study is described in detail to illustrate the depth in which artificial intelligence (AI) analytic approaches can be used to amplify the value of audio recordings. Another modality that has also attracted research interest are ocular scans that have near term potential for validation as a digital biomarker and a point of entry for clinical care workflows. Single modality measures, however, are rapidly giving way to multi-modality sensors that are embedded in all smartphones and other internet-of-things connected devices. Artificial intelligence (AI) driven analytic approaches are able to divine clinical signals from these high dimensional digital data streams. These data driven findings are setting the stage for a future state in which AD/ADRD detection will be possible at the earliest possible stage of the neurodegenerative process and enable interventions that would significantly attenuate or alter the trajectory, preventing disease from reaching the clinical diagnosis threshold.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Wang, L., Glass, J., Kourtis, L. C., & Au, R. (2025). Multi-modal data analysis for early detection of alzheimer’s disease and related dementias. The Journal of Prevention of Alzheimer s Disease. https://doi.org/10.1016/j.tjpad.2025.100399
Vancouver
Wang L, Glass J, Kourtis LC, Au R. Multi-modal data analysis for early detection of alzheimer’s disease and related dementias. The Journal of Prevention of Alzheimer s Disease. 2025. doi:10.1016/j.tjpad.2025.100399. PMID: 41326294.
BibTeX
@article{wang2025-41326294,
author = {Wang, Liming and Glass, Jim and Kourtis, Lampros C. and Au, Rhoda},
title = {{Multi-modal data analysis for early detection of alzheimer’s disease and related dementias}},
journal = {The Journal of Prevention of Alzheimer s Disease},
year = {2025},
doi = {10.1016/j.tjpad.2025.100399},
pmid = {41326294},
url = {https://doi.org/10.1016/j.tjpad.2025.100399}
}
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Key findings
- Until recently, accurate early detection of clinical symptoms associated with Alzheimer's disease (AD) and related dementias (ADRD) has been difficult.
- These data driven findings are setting the stage for a future state in which AD/ADRD detection will be possible at the earliest possible stage of the neurodegenerative process and enable interventions that would significantly attenuate or alter the trajectory, preventing disease from reaching the clinical diagnosis threshold.
Why it matters
This review describes how digital technologies such as speech recordings, ocular scans, and multi-modality sensors with artificial intelligence could enable earlier detection of Alzheimer's disease and related dementias. It discusses a future state of detection and intervention but does not itself test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 40/100
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 (The Journal of Prevention of Alzheimer s Disease) |
| Journal's citation record | 0 / 4 | The Journal of Prevention of Alzheimer s Disease: journal h-index 63 (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 | 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 | 2 / 7 | Cited 5 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 3.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 5 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Rhoda Au: h-index 90 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Massachusetts Institute of Technology: institution h-index 2,557 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41326294) |
| Free to read | 2 / 2 | Free to read |
| Total | 40 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.tjpad.2025.100399
- PubMed: 41326294
- Original source →
- Free full text: The Journal of Prevention of Alzheimer s Disease →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
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