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

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

Second, we trained XGBoost 1.0.0 models (developed by Chen and Guestrin [17]) to use a child’s photo features to respectively predict the child’s age and raw VMI score. Res Net-50 extracted 2048 features from the photos and the XGBoost model predicted age and raw VMI scores. All combinations of the extracted photo features (from 1 photo alone to 8 photographs together) were respectively fitted to the XGBoost models.

Chien-Yu Huang, Yen-Ting Yu, Kuan-Lin Chen, Jenn-Jier Lien, Gong-Hong Lin, Ching-Lin Hsieh

JMIR Form Res 2025;9:e58421