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A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease
Minwoo Lee, Hye Weon Kim, Yeong Sim Choe, Hyeon Sik Yang, Jiyeon Lee, Hyunji Lee, Jung Hyeon Yong, Donghyeon Kim et al.. Scientific Reports, 2024-05-29
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.
Key findings
- As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features.
Why it matters
This study developed a machine learning model to predict conversion from mild cognitive impairment to Alzheimer's disease using imaging and demographic features. It does not test any treatment, cure, or prevention, and it suggests the model could be useful for early diagnosis and treatment planning.
Study details
- Participants196
- Evidence strengthLow
How this study scored 53/100
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 (Scientific Reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (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 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 196 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 43 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 10.8 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 4 / 6 | Cited 11.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 2 / 4 | Cited 43 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Hyun Kook Lim: h-index 33 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Kyung Hee University: institution h-index 468 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 11 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38806509) |
| Free to read | 2 / 2 | Free to read |
| Total | 53 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-024-60134-2
- PubMed: 38806509
- 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. 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
Lee, M., Kim, H. W., Choe, Y. S., Yang, H. S., Lee, J., Lee, H., et al. (2024). A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease. Scientific Reports. https://doi.org/10.1038/s41598-024-60134-2
Vancouver
Lee M, Kim HW, Choe YS, Yang HS, Lee J, Lee H, et al. A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease. Scientific Reports. 2024. doi:10.1038/s41598-024-60134-2. PMID: 38806509.
BibTeX
@article{lee2024-38806509,
author = {Lee, Minwoo and Kim, Hye Weon and Choe, Yeong Sim and Yang, Hyeon Sik and Lee, Jiyeon and Lee, Hyunji and Yong, Jung Hyeon and Kim, Donghyeon and others},
title = {{A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease}},
journal = {Scientific Reports},
year = {2024},
doi = {10.1038/s41598-024-60134-2},
pmid = {38806509},
url = {https://doi.org/10.1038/s41598-024-60134-2}
}
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