Home › Research › Digital health tools
Machine learning–enhanced screening funnel for clinical trials in Alzheimer's disease
Scott Gladstein, Liuqing Yang, Dustin W. Wooten, Xin Huang, Robert A. Comley, Qi Yong Guo, the Alzheimer's Disease Neuroimaging Initiative. Alzheimer s & Dementia Translational Research & Clinical Interventions, 2025-04-01
Topic: Amyloid, tau and blood-test diagnosis
This approach predicts clinical progression (2-year Clinical Dementia Rating Sum of Boxes change > 1) with an area under the curve of 0.836. Incorporating it into trials (with maximized sensitivity/specificity optimization) could reduce the number of subjects required by 55%, shorten recruitment by 13 months, and reduce screening amyloid positron emission tomography scans by 72%. By reducing patient burden and shortening timelines in clinical trials, this enhanced screening funnel could accelerate the development of AD therapies. Highlights: An innovative screening funnel was developed to improve Alzheimer's disease clinical trial efficiency.The funnel incorporates machine learning (ML)-based disease progression models.The ML model identifies patients with progression rate optimal for clinical trials.Unsuitable patients would fail early in the funnel before burdensome imaging procedures.This screening funnel is customizable to specific study needs.
Key findings
- This approach predicts clinical progression (2-year Clinical Dementia Rating Sum of Boxes change > 1) with an area under the curve of 0.836.
- Incorporating it into trials (with maximized sensitivity/specificity optimization) could reduce the number of subjects required by 55%, shorten recruitment by 13 months, and reduce screening amyloid positron emission tomography scans by 72%.
- By reducing patient burden and shortening timelines in clinical trials, this enhanced screening funnel could accelerate the development of AD therapies.
- Highlights: An innovative screening funnel was developed to improve Alzheimer's disease clinical trial efficiency.The funnel incorporates machine learning (ML)-based disease progression models.The ML model identifies patients with progression rate optimal for clinical trials.Unsuitable patients would fail early in the funnel before burdensome imaging procedures.This screening funnel is customizable to specific study needs.
Why it matters
This screening approach uses machine learning to predict which patients will progress, and could cut the number of subjects needed by 55% and shorten recruitment by 13 months. It is a trial-design tool, not a treatment, so it may speed up testing of therapies rather than cure or prevent Alzheimer's itself.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Evidence strengthLow
How this study scored 33/100
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 senior author's track record, so that was left out: it earned 32 of the 96 points we could judge it on, which is 33 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s & Dementia Translational Research & Clinical Interventions) |
| Journal's citation record | 1 / 4 | Alzheimer s & Dementia Translational Research & Clinical Interventions: journal h-index 84 (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 | 1 / 7 | Cited 4 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 2.5× 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 4 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 0 / 3 | AbbVie (United States): institution h-index 443 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40276322) |
| Free to read | 2 / 2 | Free to read |
| Points | 32 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 32 of the 96 points we could judge it on is 33 out of 100. |
| Total | 33 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1002/trc2.70084
- PubMed: 40276322
- Original source →
- Free full text: Alzheimer s & Dementia Translational Research & Clinical Interventions →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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.
APA 7th
Gladstein, S., Yang, L., Wooten, D. W., Huang, X., Comley, R. A., Guo, Q. Y., et al. (2025). Machine learning–enhanced screening funnel for clinical trials in Alzheimer's disease. Alzheimer s & Dementia Translational Research & Clinical Interventions. https://doi.org/10.1002/trc2.70084
Vancouver
Gladstein S, Yang L, Wooten DW, Huang X, Comley RA, Guo QY, et al. Machine learning–enhanced screening funnel for clinical trials in Alzheimer's disease. Alzheimer s & Dementia Translational Research & Clinical Interventions. 2025. doi:10.1002/trc2.70084. PMID: 40276322.
BibTeX
@article{gladstein2025-40276322,
author = {Gladstein, Scott and Yang, Liuqing and Wooten, Dustin W. and Huang, Xin and Comley, Robert A. and Guo, Qi Yong and {the Alzheimer's Disease Neuroimaging Initiative}},
title = {{Machine learning–enhanced screening funnel for clinical trials in Alzheimer's disease}},
journal = {Alzheimer s \& Dementia Translational Research \& Clinical Interventions},
year = {2025},
doi = {10.1002/trc2.70084},
pmid = {40276322},
url = {https://doi.org/10.1002/trc2.70084}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.