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

Background: Origami is a popular activity among preschool children and can be used by therapists as an evaluation tool to assess children's development in clinical settings. It is easy to implement, appealing to children, and time-efficient, requiring only simple materials-pieces of paper. Furthermore, the products of origami may reflect children's ages and their visual-motor integration (VMI) development. However, therapists typically evaluate children's origami creations based primarily on their personal background knowledge and clinical experience, leading to subjective and descriptive feedback. Consequently, the effectiveness of using origami products to determine children's age and VMI development lacks empirical support.

Objective: This study had two main aims. First, we sought to apply artificial intelligence (AI) techniques to origami products to predict children's ages and VMI development, including VMI level (standardized scores) and VMI developmental status (typical, borderline, or delayed). Second, we explored the performance of the AI models using all combinations of photographs taken from different angles.

Methods: A total of 515 children aged 2-6 years were recruited and divided into training and testing groups at a 4:1 ratio. Children created origami dogs, which were photographed from 8 different angles. The Beery-Buktenica Developmental Test of Visual-Motor Integration, 6th Edition, was used to assess the children's VMI levels and developmental status. Three AI models-ResNet-50, XGBoost, and a multilayer perceptron-were combined sequentially to predict age z scores and VMI z scores using the training group. The trained models were then tested using the testing group, and the accuracy of the predicted VMI developmental status was also calculated.

Results: The R2 of the age and the VMI trained models ranged from 0.50 to 0.73 and from 0.50 to 0.66, respectively. The AI models that obtained an R2>0.70 for the age model and an R2>0.60 for the VMI model were selected for model testing. Those models were further examined for the accuracy of the VMI developmental status, the correlations, and the mean absolute error (MAE) of both the age and the VMI models. The accuracy of the VMI developmental status was about 71%-76%. The correlations between the final predicted age z score and the real age z score ranged from 0.84 to 0.85, and the correlations of the final predicted VMI z scores to the real z scores ranged from 0.77 to 0.81. The MAE of the age models ranged from 0.42 to 0.46 and those of the VMI models ranged from 0.43 to 0.48.

Conclusions: Our findings indicate that AI techniques have a significant potential for predicting children's development. The insights provided by AI may assist therapists in better interpreting children's performance in activities.