The potential of evaluating shape drawing using machine learning for predicting high autistic traits.

Journal: PloS one
PMID: 40203018

Abstract

BACKGROUND: Children with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product characteristics can be objectively evaluated. This study aimed to assess the potential of evaluating shape drawing using machine learning to predict high autistic traits.

Authors

  • Yoshimasa Ohmoto
    Faculty of Informatics, Department of Behavior Informatics, Shizuoka University, Shizuoka, Japan.
  • Kazunori Terada
    Gifu University, Gifu, Yanagido, Japan.
  • Hitomi Shimizu
    Department of Neuropsychiatry, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan.
  • Akira Imamura
    Unit of Medical Science, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Ryoichiro Iwanaga
    Unit of Medical Science, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Hirokazu Kumazaki
    Department of Clinical Research on Social Recognition and Memory, Research Center for Child Mental Development, Kanazawa University, Ishikawa, Japan.

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