Annals of medicine · 2025
Using a coloring activity to identify children's development of visual-motor integration: an application of artificial intelligence.
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Huang Tzu-Yun, Chen Kuan-Lin, Lin Gong-Hong, Huang Chien-Yu
Аннотация
AIM: Visual-motor integration (VMI) is an important indicator in children with learning disabilities. We aimed to use performance in a coloring activity to identify children's VMI developmental status.
METHODS: A sample of 505 preschool children (mean = 57.64, SD = 11.10) were recruited. Among them, data from 404 and 101 children were used as the training and testing data, respectively. The Beery-Buktenica Developmental Test of Visual-motor Integration, fourth Edition, (VMI-4) was used as an indicator for the model of artificial intelligence (AI). The total scores of the VMI-4 were calculated, and then based on the children's age, the total scores were transferred into standard scores and the developmental status of visual-motor integration. The AI model comprised a regression model and classification model to predict the developmental status rated by the VMI-4.
RESULTS: In the training data, we found that the AI model comprising the support vector machine (SVM) regression model and eXtreme Gradient Boostin (XGBoost) classification model exhibited the best performance (accuracy: 86.2%; sensitivity: 84.7%; and specificity, 85.4%). The results of the trained AI model on the testing data indicated good performance, with accuracy, sensitivity, and specificity of 80.20%, 73.68%, and 81.71%, respectively.
CONCLUSIONS: Combining the coloring activity with the AI technique has great potential as a screening tool to identify children's VMI developmental status.
METHODS: A sample of 505 preschool children (mean = 57.64, SD = 11.10) were recruited. Among them, data from 404 and 101 children were used as the training and testing data, respectively. The Beery-Buktenica Developmental Test of Visual-motor Integration, fourth Edition, (VMI-4) was used as an indicator for the model of artificial intelligence (AI). The total scores of the VMI-4 were calculated, and then based on the children's age, the total scores were transferred into standard scores and the developmental status of visual-motor integration. The AI model comprised a regression model and classification model to predict the developmental status rated by the VMI-4.
RESULTS: In the training data, we found that the AI model comprising the support vector machine (SVM) regression model and eXtreme Gradient Boostin (XGBoost) classification model exhibited the best performance (accuracy: 86.2%; sensitivity: 84.7%; and specificity, 85.4%). The results of the trained AI model on the testing data indicated good performance, with accuracy, sensitivity, and specificity of 80.20%, 73.68%, and 81.71%, respectively.
CONCLUSIONS: Combining the coloring activity with the AI technique has great potential as a screening tool to identify children's VMI developmental status.