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Automatic dementia screening and scoring by applying deep learning on clock-drawing tests
Chen S, Stromer D, Alabdalrahim HA, Schwab S, Weih M, Maier A.. Scientific reports, 2020-11-30
Topic: Risk, prevention and care in population studies
Dementia is one of the most common neurological syndromes in the world. Usually, diagnoses are made based on paper-and-pencil tests and scored depending on personal judgments of experts. This technique can introduce errors and has high inter-rater variability. To overcome these issues, we present an automatic assessment of the widely used paper-based clock-drawing test by means of deep neural networks. Our study includes a comparison of three modern architectures: VGG16, ResNet-152, and DenseNet-121. The dataset consisted of 1315 individuals. To deal with the limited amount of data, which also included several dementia types, we used optimization strategies for training the neural network. The outcome of our work is a standardized and digital estimation of the dementia screening result and severity level for an individual. We achieved accuracies of 96.65% for screening and up to 98.54% for scoring, overcoming the reported state-of-the-art as well as human accuracies. Due to the digital format, the paper-based test can be simply scanned by using a mobile device and then be evaluated also in areas where there is a staff shortage or where no clinical experts are available.
Key findings
- The outcome of our work is a standardized and digital estimation of the dementia screening result and severity level for an individual.
Why it matters
Deep learning applied to clock-drawing tests achieved high accuracy for dementia screening and scoring, which could allow automated assessment where clinical experts are unavailable. This is a screening and scoring tool, not a treatment, cure or prevention for dementia.
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Study details
- Participants1,315
- Effect size96.65%; 98.54%
- Evidence strengthLow
How this study scored 54/100
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 (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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,315 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 95 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 11.9 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 6.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 3 / 4 | Cited 57 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Andreas Maier: h-index 57 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Friedrich-Alexander-Universität Erlangen-Nürnberg: institution h-index 901 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33257744) |
| 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
- DOI: 10.1038/s41598-020-74710-9
- PubMed: 33257744
- 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. Check the citation against the original before you use it.
APA 7th
Chen, S., Stromer, D., Alabdalrahim, H. A., Schwab, S., Weih, M., & Maier, A. (2020). Automatic dementia screening and scoring by applying deep learning on clock-drawing tests. Scientific reports. https://doi.org/10.1038/s41598-020-74710-9
Vancouver
Chen S, Stromer D, Alabdalrahim HA, Schwab S, Weih M, Maier A. Automatic dementia screening and scoring by applying deep learning on clock-drawing tests. Scientific reports. 2020. doi:10.1038/s41598-020-74710-9. PMID: 33257744.
BibTeX
@article{chen2020-33257744,
author = {Chen, S. and Stromer, D. and Alabdalrahim, H. A. and Schwab, S. and Weih, M. and Maier, A.},
title = {{Automatic dementia screening and scoring by applying deep learning on clock-drawing tests}},
journal = {Scientific reports},
year = {2020},
doi = {10.1038/s41598-020-74710-9},
pmid = {33257744},
url = {https://doi.org/10.1038/s41598-020-74710-9}
}
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