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AI-based differential diagnosis of dementia etiologies on multimodal data
Xue C, Kowshik SS, Lteif D, Puducheri S, Jasodanand VH, Zhou OT, Walia AS, Guney OB et al.. Nature medicine, 2024-07-04
Topic: Amyloid, tau and blood-test diagnosis
Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Xue, C., Kowshik, S. S., Lteif, D., Puducheri, S., Jasodanand, V. H., Zhou, O. T., et al. (2024). AI-based differential diagnosis of dementia etiologies on multimodal data. Nature medicine. https://doi.org/10.1038/s41591-024-03118-z
Vancouver
Xue C, Kowshik SS, Lteif D, Puducheri S, Jasodanand VH, Zhou OT, et al. AI-based differential diagnosis of dementia etiologies on multimodal data. Nature medicine. 2024. doi:10.1038/s41591-024-03118-z. PMID: 38965435.
BibTeX
@article{xue2024-38965435,
author = {Xue, C. and Kowshik, S. S. and Lteif, D. and Puducheri, S. and Jasodanand, V. H. and Zhou, O. T. and Walia, A. S. and Guney, O. B. and others},
title = {{AI-based differential diagnosis of dementia etiologies on multimodal data}},
journal = {Nature medicine},
year = {2024},
doi = {10.1038/s41591-024-03118-z},
pmid = {38965435},
url = {https://doi.org/10.1038/s41591-024-03118-z}
}
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.
Key findings
- Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies.
Why it matters
An AI model using demographics, medical history, neuropsychological assessments, and multimodal neuroimaging across 51,269 participants identified 10 dementia etiologies, with a microaveraged AUROC of 0.96 for differentiating etiologies. This is a diagnostic tool, not a treatment, cure, or prevention for Alzheimer's disease.
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Study details
- Participants51,269
- Effect size26.25%
- Evidence strengthLow
How this study scored 71/100
Published in a top-tier journal. Scored zero for study design and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 10 / 10 | Tier 1 — top-tier general journal (Nature medicine) |
| Journal's citation record | 4 / 4 | Nature Medicine: journal h-index 763 (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 | 4 / 4 | 51,269 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 177 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 44.2 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 6 / 6 | Cited 48.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 15 other studies in this library |
| Still being cited | 3 / 4 | Cited 177 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Vijaya B. Kolachalama: h-index 36 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Stanford University: institution h-index 2,684 (OpenAlex) |
| Multicenter collaboration | 3 / 3 | OpenAlex lists 14 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38965435) |
| Free to read | 2 / 2 | Free to read |
| Total | 71 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41591-024-03118-z
- PubMed: 38965435
- Original source →
- Free full text: Nature Medicine →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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