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Enhancing dementia risk screening with GAN-synthesized periodontal examination and general blood test data
Katsunori Oyama, Toshiki Isogai, Yohei Nakayama, Ryoki Kobayashi, Daisuke Kitano, Kenji Karako, Kaoru Sakatani. Frontiers in neurology, 2024-08-14
Topic: Brain imaging, connectivity and assessment
This study used measured data from 108 participants and the synthesized data generated from the measured data. External validity was evaluated using a different dataset of 41 participants with missing items. The results suggested that normal GANs have the advantage of investigating models in data diversity, whereas CTGANs preserve the data structure and linear relationships in tabular data from the measured data, which drastically improves linear regression models. Importantly, by interpolating sparse areas in the distribution, such as age, the synthesized models maintained prediction accuracy for test data with extreme inputs. These findings suggest that GAN-synthesized data can effectively address regression problems and improve dementia risk prediction.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Oyama, K., Isogai, T., Nakayama, Y., Kobayashi, R., Kitano, D., Karako, K., et al. (2024). Enhancing dementia risk screening with GAN-synthesized periodontal examination and general blood test data. Frontiers in neurology. https://doi.org/10.3389/fneur.2024.1379916
Vancouver
Oyama K, Isogai T, Nakayama Y, Kobayashi R, Kitano D, Karako K, et al. Enhancing dementia risk screening with GAN-synthesized periodontal examination and general blood test data. Frontiers in neurology. 2024. doi:10.3389/fneur.2024.1379916. PMID: 39206296.
BibTeX
@article{oyama2024-39206296,
author = {Oyama, Katsunori and Isogai, Toshiki and Nakayama, Yohei and Kobayashi, Ryoki and Kitano, Daisuke and Karako, Kenji and Sakatani, Kaoru},
title = {{Enhancing dementia risk screening with GAN-synthesized periodontal examination and general blood test data}},
journal = {Frontiers in neurology},
year = {2024},
doi = {10.3389/fneur.2024.1379916},
pmid = {39206296},
url = {https://doi.org/10.3389/fneur.2024.1379916}
}
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 7 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.
Key findings
- This study used measured data from 108 participants and the synthesized data generated from the measured data.
- External validity was evaluated using a different dataset of 41 participants with missing items.
- The results suggested that normal GANs have the advantage of investigating models in data diversity, whereas CTGANs preserve the data structure and linear relationships in tabular data from the measured data, which drastically improves linear regression models.
- Importantly, by interpolating sparse areas in the distribution, such as age, the synthesized models maintained prediction accuracy for test data with extreme inputs.
- These findings suggest that GAN-synthesized data can effectively address regression problems and improve dementia risk prediction.
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Study details
- Participants108
- Evidence strengthLow
How this study scored 52/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multicenter collaboration and free to read, so those were left out: it earned 29 of the 56 points we could judge it on, which is 52 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in neurology) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 108 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) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39206296) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 29 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 29 of the 56 points we could judge it on is 52 out of 100. |
| Total | 52 / 100 | — |
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