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54/100

✓ Peer-reviewed alzhub usefulness score 54 out of 100. Long-term cohort studies

Topic: Amyloid, tau and blood-test diagnosis

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression.

Key findings

In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs.
For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression.
We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC).
The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer's dementia.

Why it matters

This study developed a deep learning model that uses structural MRI scans to distinguish mild Alzheimer's dementia from mild cognitive impairment and normal cognition. It is a diagnostic tool and does not treat, cure, or prevent the disease.

Study details

Evidence strengthModerate

How this study scored 54/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, 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	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
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	3 / 5	2022 — within 6 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	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
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	5 / 7	Cited 195 times (OpenAlex, 2026-10-10)
Citations per year	4 / 5	32.5 citations a year over 6 year(s) since 2022
Impact for its field and year	5 / 6	Cited 21.5× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	2 / 6	Cited by 8 other studies in this library
Still being cited	3 / 4	Cited 170 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Narges Razavian: h-index 23 (OpenAlex, whole career)
Institution's track record	2 / 3	New York University: institution h-index 1,612 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 3 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 36253382)
Free to read	2 / 2	Free to read
Total	54 / 100	—

What each of these 20 components measures, and what this score is not →

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 names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.

APA 7th

Liu, S., Masurkar, A. V., Rusinek, H., Chen, J., Zhang, B., Zhu, W., et al. (2022). Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs. Scientific reports. https://doi.org/10.1038/s41598-022-20674-x

Vancouver

Liu S, Masurkar AV, Rusinek H, Chen J, Zhang B, Zhu W, et al. Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs. Scientific reports. 2022. doi:10.1038/s41598-022-20674-x. PMID: 36253382.

BibTeX

@article{liu2022-36253382,
  author  = {Liu, S. and Masurkar, A. V. and Rusinek, H. and Chen, J. and Zhang, B. and Zhu, W. and Fernandez-Granda, C. and Razavian, N.},
  title   = {{Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs}},
  journal = {Scientific reports},
  year    = {2022},
  doi     = {10.1038/s41598-022-20674-x},
  pmid    = {36253382},
  url     = {https://doi.org/10.1038/s41598-022-20674-x}
}

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