Home › Research › Digital health tools
An FDG-PET–Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders
Leland Barnard, Hugo Botha, Nick Corriveau‐Lecavalier, Jonathan Graff‐Radford, Ellen Dicks, Venkatsampath Gogineni, Gemeng Zhang, Brian J. Burkett et al.. Neurology, 2025-06-27
Topic: Amyloid, tau and blood-test diagnosis
The discovery cohort contained 3,671 individuals with a mean age of 68 years and consisted of 49% reported female. Our model framework was able to detect the presence of 9 different neurodegenerative phenotypes with a sensitivity of 0.89 ± 0.03 and an area under the receiver operating characteristic curve of 0.93 ± 0.02. In the radiologic reader study, readers using our model were found to have 3.3 ± 1.1 times greater odds of making a correct diagnosis than readers using a current standard-of-care workflow. Our proposed framework provides strong classification performance with high interpretability, and it addresses many of the challenges that face clinical integration of ML-based decision support tools. One limitation of this study is a uniform discovery cohort that is not representative of other patient populations in some regards.
Key findings
- The discovery cohort contained 3,671 individuals with a mean age of 68 years and consisted of 49% reported female.
- Our model framework was able to detect the presence of 9 different neurodegenerative phenotypes with a sensitivity of 0.89 ± 0.03 and an area under the receiver operating characteristic curve of 0.93 ± 0.02.
- In the radiologic reader study, readers using our model were found to have 3.3 ± 1.1 times greater odds of making a correct diagnosis than readers using a current standard-of-care workflow.
- Our proposed framework provides strong classification performance with high interpretability, and it addresses many of the challenges that face clinical integration of ML-based decision support tools.
- One limitation of this study is a uniform discovery cohort that is not representative of other patient populations in some regards.
Why it matters
An FDG-PET-based machine learning framework detected 9 different neurodegenerative phenotypes with a sensitivity of 0.89 and an area under the curve of 0.93, and readers using the model had 3.3 times greater odds of making a correct diagnosis. This is a diagnostic support tool and does not treat, cure, or prevent Alzheimer's disease.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Participants3,671
- Evidence strengthModerate
How this study scored 58/100
Published in a leading specialty journal. Scored zero for study design, effect reported 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 56 of the 96 points we could judge it on, which is 58 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Neurology) |
| Journal's citation record | 4 / 4 | Neurology: journal h-index 525 (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 | 3 / 5 | Rated “moderate” 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 | 3 / 4 | 3,671 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 3 / 7 | Cited 23 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 14.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 2 / 4 | Cited 23 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 | 2 / 3 | Mayo Clinic: institution h-index 1,606 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40577677) |
| Free to read | 2 / 2 | Free to read |
| Points | 56 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 56 of the 96 points we could judge it on is 58 out of 100. |
| Total | 58 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1212/wnl.0000000000213831
- PubMed: 40577677
- Original source →
- Free full text: Pure Amsterdam UMC →
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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Barnard, L., Botha, H., Corriveau‐Lecavalier, N., Graff‐Radford, J., Dicks, E., Gogineni, V., et al. (2025). An FDG-PET–Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders. Neurology. https://doi.org/10.1212/wnl.0000000000213831
Vancouver
Barnard L, Botha H, Corriveau‐Lecavalier N, Graff‐Radford J, Dicks E, Gogineni V, et al. An FDG-PET–Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders. Neurology. 2025. doi:10.1212/wnl.0000000000213831. PMID: 40577677.
BibTeX
@article{barnard2025-40577677,
author = {Barnard, Leland and Botha, Hugo and Corriveau‐Lecavalier, Nick and Graff‐Radford, Jonathan and Dicks, Ellen and Gogineni, Venkatsampath and Zhang, Gemeng and Burkett, Brian J. and others},
title = {{An FDG-PET–Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders}},
journal = {Neurology},
year = {2025},
doi = {10.1212/wnl.0000000000213831},
pmid = {40577677},
url = {https://doi.org/10.1212/wnl.0000000000213831}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Later studies that cite this one
3 studies in the alzhub library list this one in their references.