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Predicting Age and Visual-Motor Integration Using Origami Photographs: Deep Learning Study.

Chien-Yu Huang1,2, Yen-Ting Yu1,3, Kuan-Lin Chen3,4,5

  • 1School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.

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|January 13, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can predict children's ages and visual-motor integration (VMI) development from origami creations. This AI approach offers objective insights for therapists assessing child development.

Keywords:
activity performanceartificial intelligencechild developmentchild development screeningchildrendeep learningdevelopmental statusorigamivisual motor integration

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Area of Science:

  • Developmental Pediatrics
  • Artificial Intelligence in Healthcare
  • Child Psychology

Background:

  • Origami is a popular, engaging activity for children, offering potential as a clinical assessment tool.
  • Current subjective methods for evaluating origami limit its utility in assessing children's age and visual-motor integration (VMI).
  • Empirical support is lacking for using origami products to objectively determine developmental status.

Purpose of the Study:

  • To apply artificial intelligence (AI) to origami products for predicting children's ages.
  • To predict children's visual-motor integration (VMI) development, including standardized scores and developmental status (typical, borderline, delayed).
  • To evaluate AI model performance using images from various photographic angles.

Main Methods:

  • 515 children aged 2-6 years created origami dogs, photographed from 8 angles.
  • AI models (ResNet-50, XGBoost, multilayer perceptron) were trained to predict age and VMI z-scores.
  • The Beery-Buktenica Developmental Test of Visual-Motor Integration, 6th Edition, assessed VMI levels.

Main Results:

  • AI models achieved R2 values up to 0.73 for age prediction and 0.66 for VMI prediction.
  • Accuracy for predicting VMI developmental status ranged from 71% to 76%.
  • High correlations were observed between predicted and actual z-scores (age: 0.84-0.85, VMI: 0.77-0.81).

Conclusions:

  • AI demonstrates significant potential for objectively predicting children's developmental progress.
  • AI-driven insights can enhance therapists' interpretation of children's performance in activities.
  • Origami, analyzed via AI, shows promise as a tool for developmental assessment.