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Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease
Jin D, Zhou B, Han Y, Ren J, Han T, Liu B, Lu J, Song C et al.. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2020-06-09
Topic: Amyloid, tau and blood-test diagnosis
Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end-to-end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross-validation on in-house, multicenter ( n = 716), and public ( n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD.
Key findings
- Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model.
Why it matters
This study developed a deep learning model using structural MRI that classified Alzheimer's disease with up to 92% accuracy and predicted which people with mild cognitive impairment progressed to Alzheimer's with 72% accuracy. It is a diagnostic and prognostic tool, not a treatment, cure or prevention.
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Study details
- Participants716
- Evidence strengthLow
How this study scored 55/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Advanced science (Weinheim, Baden-Wurttemberg, Germany)) |
| Journal's citation record | 3 / 4 | Advanced Science: journal h-index 282 (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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 716 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 | 4 / 7 | Cited 92 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 11.5 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 9.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 2 / 4 | Cited 35 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Yong Liu: h-index 65 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Stanford University: institution h-index 2,684 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 14 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32714766) |
| Free to read | 2 / 2 | Free to read |
| Total | 55 / 100 | — |
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Read the original
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
Jin, D., Zhou, B., Han, Y., Ren, J., Han, T., Liu, B., et al. (2020). Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease. Advanced science (Weinheim, Baden-Wurttemberg, Germany). https://doi.org/10.1002/advs.202000675
Vancouver
Jin D, Zhou B, Han Y, Ren J, Han T, Liu B, et al. Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease. Advanced science (Weinheim, Baden-Wurttemberg, Germany). 2020. doi:10.1002/advs.202000675. PMID: 32714766.
BibTeX
@article{jin2020-32714766,
author = {Jin, D. and Zhou, B. and Han, Y. and Ren, J. and Han, T. and Liu, B. and Lu, J. and Song, C. and others},
title = {{Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease}},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2020},
doi = {10.1002/advs.202000675},
pmid = {32714766},
url = {https://doi.org/10.1002/advs.202000675}
}
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