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Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer's Disease
Hu J, Qing Z, Liu R, Zhang X, Lv P, Wang M, Wang Y, He K et al.. Frontiers in neuroscience, 2021-01-21
Topic: Amyloid, tau and blood-test diagnosis
Frontotemporal dementia (FTD) and Alzheimer's disease (AD) have overlapping symptoms, and accurate differential diagnosis is important for targeted intervention and treatment. Previous studies suggest that the deep learning (DL) techniques have the potential to solve the differential diagnosis problem of FTD, AD and normal controls (NCs), but its performance is still unclear. In addition, existing DL-assisted diagnostic studies still rely on hypothesis-based expert-level preprocessing.
Key findings
- Previous studies suggest that the deep learning (DL) techniques have the potential to solve the differential diagnosis problem of FTD, AD and normal controls (NCs), but its performance is still unclear.
- In addition, existing DL-assisted diagnostic studies still rely on hypothesis-based expert-level preprocessing.
- On the one hand, it imposes high requirements on clinicians and data themselves; On the other hand, it hinders the backtracking of classification results to the original image data, resulting in the classification results cannot be interpreted intuitively.
- In the current study, a large cohort of 3D T1-weighted structural magnetic resonance imaging (MRI) volumes ( n = 4,099) was collected from two publicly available databases, i.e., the ADNI and the NIFD.
Why it matters
This is a technical study using deep learning to tell apart frontotemporal dementia, Alzheimer's disease, and normal controls on brain scans. It is a diagnostic tool for research and does not test any treatment, cure, or prevention.
Study details
- Participants4,099
- Effect size91.83%
- Evidence strengthModerate
How this study scored 55/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in neuroscience) |
| Journal's citation record | 2 / 4 | Frontiers in Neuroscience: journal h-index 226 (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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 3 / 4 | 4,099 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) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 70 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.8 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 8.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 5 other studies in this library |
| Still being cited | 2 / 4 | Cited 36 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Bing Zhang: h-index 24 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Nanjing University: institution h-index 850 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33551735) |
| Free to read | 2 / 2 | Free to read |
| Total | 55 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fnins.2020.626154
- PubMed: 33551735
- Original source →
- Free full text: Frontiers in Neuroscience →
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
Hu, J., Qing, Z., Liu, R., Zhang, X., Lv, P., Wang, M., et al. (2020). Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer's Disease. Frontiers in neuroscience. https://doi.org/10.3389/fnins.2020.626154
Vancouver
Hu J, Qing Z, Liu R, Zhang X, Lv P, Wang M, et al. Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer's Disease. Frontiers in neuroscience. 2020. doi:10.3389/fnins.2020.626154. PMID: 33551735.
BibTeX
@article{hu2020-33551735,
author = {Hu, J. and Qing, Z. and Liu, R. and Zhang, X. and Lv, P. and Wang, M. and Wang, Y. and He, K. and others},
title = {{Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer's Disease}},
journal = {Frontiers in neuroscience},
year = {2020},
doi = {10.3389/fnins.2020.626154},
pmid = {33551735},
url = {https://doi.org/10.3389/fnins.2020.626154}
}
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