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A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data
Li H, Habes M, Wolk DA, Fan Y, Alzheimer's Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Study of Aging.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2019-06-11
Topic: Amyloid, tau and blood-test diagnosis
Introduction It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia. Methods A deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting. Results The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P Discussion Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.
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APA 7th
Li, H., Habes, M., Wolk, D. A., & Fan, Y. (2019). A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1016/j.jalz.2019.02.007
Vancouver
Li H, Habes M, Wolk DA, Fan Y. A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2019. doi:10.1016/j.jalz.2019.02.007. PMID: 31201098.
BibTeX
@article{li2019-31201098,
author = {Li, H. and Habes, M. and Wolk, D. A. and Fan, Y.},
title = {{A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2019},
doi = {10.1016/j.jalz.2019.02.007},
pmid = {31201098},
url = {https://doi.org/10.1016/j.jalz.2019.02.007}
}
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Key findings
- Results The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]).
- The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002).
Why it matters
This deep learning model predicted progression from mild cognitive impairment to Alzheimer's dementia from hippocampal MRI data, with a concordance index of 0.762 in one testing group and 0.781 in another. It is a prognostic tool for identifying likely progressors, not a treatment or cure.
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Study details
- Participants2,146
- Evidence strengthLow
How this study scored 56/100
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 54 of the 96 points we could judge it on, which is 56 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Alzheimer's & dementia : the journal of the Alzheimer's Association) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (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 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 3 / 4 | 2,146 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 | 5 / 7 | Cited 269 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 29.9 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 5 / 6 | Cited 22.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 7 other studies in this library |
| Still being cited | 3 / 4 | Cited 104 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 | University of Pennsylvania: institution h-index 1,859 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31201098) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 54 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 54 of the 96 points we could judge it on is 56 out of 100. |
| Total | 56 / 100 | — |
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