Machine learning trial to detect sex differences in simple sticker arts of 1606 preschool children.

Journal: Minerva pediatrics
Published Date: Jun 1, 2024

Abstract

BACKGROUND: Previous studies suggested that drawings made by preschool boys and girls show distinguishable differences. However, children's drawings on their own are too complexly determined and inherently ambiguous to be a reliable indicator. In the present study, we attempted to develop a machine learning algorithm for classification of sex of the subjects using children's artworks.

Authors

  • Keiko Matsubara
    Department of Molecular Endocrinology, National Center for Child Health and Development, Tokyo, Japan.
  • Yuko Ohgami
    Department of Child Development and Education, Faculty of Humanities, Wayo Women's University, Chiba, Japan.
  • Koji Okamura
    Department of Systems BioMedicine, National Research Institute for Child Health and Development, Setagaya, Tokyo, Japan.
  • Saki Aoto
    Medical Genome Center, National Center for Child Health and Development, Tokyo, Japan.
  • Maki Fukami
    Department of Molecular Endocrinology, National Research Institute for Child Health and Development, Tokyo, Japan - fukami-m@ncchd.go.jp.
  • Yukiko Shimada
    Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.

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