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Predicting Alzheimer's disease progression using multi-modal deep learning approach
Lee G, Nho K, Kang B, Sohn KA, Kim D, for Alzheimer’s Disease Neuroimaging Initiative.. Scientific reports, 2019-02-13
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.
Key findings
- Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data.
Why it matters
This study used a deep learning model combining imaging, spinal fluid, and cognitive data to predict which people with MCI would convert to Alzheimer's disease, reaching 81% accuracy. It is a prediction tool for identifying people for trials, not a treatment, cure, or prevention.
Study details
- Study designcross-sectional
- Effect sizeAUC = 0.86
- Evidence strengthModerate
How this study scored 66/100
Published in a peer-reviewed journal, and it is a snapshot of people at one point in time. Scored zero for sample size, 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 (Scientific reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (OpenAlex, 2026-10-29) |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| 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 | 2 / 5 | 2019 — within 10 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 | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 | 6 / 7 | Cited 404 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 44.9 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 5 / 6 | Cited 28.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 14 other studies in this library |
| Still being cited | 3 / 4 | Cited 176 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author David Geldmacher: h-index 55 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | University of Washington: institution h-index 3,540 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 39 institution(s) in 4 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30760848) |
| Free to read | 2 / 2 | Free to read |
| Total | 66 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-018-37769-z
- PubMed: 30760848
- Original source →
- Free full text: Scientific Reports →
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. Check the citation against the original before you use it.
APA 7th
Lee, G., Nho, K., Kang, B., Sohn, K. A., Kim, D., & for Alzheimer’s Disease Neuroimaging Initiative. (2019). Predicting Alzheimer's disease progression using multi-modal deep learning approach. Scientific reports. https://doi.org/10.1038/s41598-018-37769-z
Vancouver
Lee G, Nho K, Kang B, Sohn KA, Kim D, for Alzheimer’s Disease Neuroimaging Initiative. Predicting Alzheimer's disease progression using multi-modal deep learning approach. Scientific reports. 2019. doi:10.1038/s41598-018-37769-z. PMID: 30760848.
BibTeX
@article{lee2019-30760848,
author = {Lee, G. and Nho, K. and Kang, B. and Sohn, K. A. and Kim, D. and {for Alzheimer’s Disease Neuroimaging Initiative}},
title = {{Predicting Alzheimer's disease progression using multi-modal deep learning approach}},
journal = {Scientific reports},
year = {2019},
doi = {10.1038/s41598-018-37769-z},
pmid = {30760848},
url = {https://doi.org/10.1038/s41598-018-37769-z}
}
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