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Concurrent detection of cognitive impairment and amyloid positivity with a multimodal machine learning-enabled digital cognitive assessment
Jannati A, Thompson K, Toro-Serey C, Gomes-Osman J, Banks RE, Higgins C, Showalter J, Bates D et al.. Alzheimer's research & therapy, 2025-12-11
Topic: Amyloid, tau and blood-test diagnosis
Background Early identification of cognitive impairment and brain pathology associated with Alzheimer's disease (AD) is essential to maximize benefits from lifestyle interventions and emerging pharmacologic disease-modifying treatments (DMT). Digital cognitive assessments (DCAs) can quickly capture an array of metrics that can be used to train machine-learning (ML) models to concurrently evaluate different outcomes. DCAs have the potential to optimize clinical workflows and enable efficient assessment of cognitive function and the likelihood of a given underlying pathology. Methods We assessed the ability of a multimodal ML-enabled DCA, the Digital Clock and Recall (DCR), to concurrently estimate brain amyloid-beta (Aβ) status and detect cognitive impairment, as compared with traditional cognitive assessments, including the MMSE, RAVLT, a DCA, Cognivue ® , and blood-based biomarkers in 930 participants from the Bio-Hermes-001 clinical study. Results Aβ42/40, p-tau181, APS, and p-tau217 poorly classified cognitive impairment (AUCs: 0.61; 0.63; 0.63; 0.70, respectively), but accurately classified Aβ status (AUCs: 0.81; 0.78; 0.85, 0.89, respectively). MMSE, RAVLT, and Cognivue poorly classified Aβ status (AUCs: 0.70, 0.73, 0.70, respectively). However, separate multimodal, DCR-based ML classification models, run in parallel, accurately classified both cognitive impairment (AUC = 0.83) and Aβ-PET status (AUC = 0.81). Conclusions DCAs that leverage digital technologies to generate advanced metrics, such as the DCR, enable accurate and efficient detection of cognitive impairment associated with AD pathology. They have the potential to empower health systems and primary care providers to help their patients make timely treatment decisions.
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APA 7th
Jannati, A., Thompson, K., Toro-Serey, C., Gomes-Osman, J., Banks, R. E., Higgins, C., et al. (2025). Concurrent detection of cognitive impairment and amyloid positivity with a multimodal machine learning-enabled digital cognitive assessment. Alzheimer's research & therapy. https://doi.org/10.1186/s13195-025-01913-5
Vancouver
Jannati A, Thompson K, Toro-Serey C, Gomes-Osman J, Banks RE, Higgins C, et al. Concurrent detection of cognitive impairment and amyloid positivity with a multimodal machine learning-enabled digital cognitive assessment. Alzheimer's research & therapy. 2025. doi:10.1186/s13195-025-01913-5. PMID: 41382170.
BibTeX
@article{jannati2025-41382170,
author = {Jannati, A. and Thompson, K. and Toro-Serey, C. and Gomes-Osman, J. and Banks, R. E. and Higgins, C. and Showalter, J. and Bates, D. and others},
title = {{Concurrent detection of cognitive impairment and amyloid positivity with a multimodal machine learning-enabled digital cognitive assessment}},
journal = {Alzheimer's research \& therapy},
year = {2025},
doi = {10.1186/s13195-025-01913-5},
pmid = {41382170},
url = {https://doi.org/10.1186/s13195-025-01913-5}
}
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
- Background Early identification of cognitive impairment and brain pathology associated with Alzheimer's disease (AD) is essential to maximize benefits from lifestyle interventions and emerging pharmacologic disease-modifying treatments (DMT).
- Methods We assessed the ability of a multimodal ML-enabled DCA, the Digital Clock and Recall (DCR), to concurrently estimate brain amyloid-beta (Aβ) status and detect cognitive impairment, as compared with traditional cognitive assessments, including the MMSE, RAVLT, a DCA, Cognivue ® , and blood-based biomarkers in 930 participants from the Bio-Hermes-001 clinical study.
- Results Aβ42/40, p-tau181, APS, and p-tau217 poorly classified cognitive impairment (AUCs: 0.61; 0.63; 0.63; 0.70, respectively), but accurately classified Aβ status (AUCs: 0.81; 0.78; 0.85, 0.89, respectively).
- Conclusions DCAs that leverage digital technologies to generate advanced metrics, such as the DCR, enable accurate and efficient detection of cognitive impairment associated with AD pathology.
Study details
- Participants930
- Effect sizeAUC = 0.83; AUC = 0.81
- Evidence strengthLow
How this study scored 59/100
Published in a peer-reviewed journal. Scored zero for study design 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 34 of the 58 points we could judge it on, which is 59 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer's research & therapy) |
| 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 | 2 / 4 | 930 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 | — / 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 41382170) |
| Free to read | 2 / 2 | Free to read |
| Points | 34 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 34 of the 58 points we could judge it on is 59 out of 100. |
| Total | 59 / 100 | — |
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