The potential of evaluating shape drawing using machine learning for predicting high autistic traits
These results demonstrate the potential of assessing shape characteristics using machine learning to predict high levels of autistic traits. Future studies with a wider variety of shapes are warranted to establish further the potential efficacy of drawing skills for screening for autism spectrum conditions.
Authors
- Yoshimasa Ohmoto (ORCID: https://orcid.org/0000-0003-2962-6675)
- Ryoichiro Iwanaga (ORCID: https://orcid.org/0000-0001-6151-8768)
- Kazunori Terada (ORCID: https://orcid.org/0000-0002-8728-0943)
- Akira Imamura (ORCID: https://orcid.org/0000-0002-6373-7160)
- Hirokazu Kumazaki (ORCID: https://orcid.org/0000-0002-2567-0276)
- Hitomi Shimizu
Institutions
- Shizuoka University (JP)
- Gifu University (JP)
- Nagasaki University (JP)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-04-09
- DOI
- https://doi.org/10.1371/journal.pone.0320770
- Citations
- 1
- Primary Topic
- Autism Spectrum Disorder Research
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- Japan Research Promotion Society for Cardiovascular Diseases
- Japan Society for the Promotion of Science
- JST-Mirai Program