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Using a coloring activity to identify children's development of visual-motor integration: an application of
Tzu-Yun Huang1,2, Kuan-Lin Chen1,2,3, Gong-Hong Lin4
1Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan.
Insights
This study developed an AI model using a coloring activity to assess visual-motor integration (VMI) in preschoolers. The AI model shows promise as a screening tool for identifying VMI developmental status in children.
Area of Science:
- Pediatric developmental assessment
- Artificial intelligence in healthcare
- Childhood learning disabilities
Background:
- Visual-motor integration (VMI) is crucial for children's development and learning.
- Difficulties in VMI are often associated with learning disabilities.
- Early identification of VMI status is essential for timely intervention.
Purpose of the Study:
- To evaluate the potential of a coloring activity for assessing VMI in preschool children.
- To develop and validate an artificial intelligence (AI) model for predicting VMI developmental status.
- To establish a novel, accessible screening method for VMI.
Main Methods:
- Utilized data from 505 preschool children, with 404 for training and 101 for testing.
- Employed the Beery-Buktenica Developmental Test of Visual-motor Integration, fourth Edition (VMI-4) as a benchmark.
- Developed an AI model incorporating support vector machine (SVM) regression and eXtreme Gradient Boosting (XGBoost) classification.
Main Results:
- The AI model achieved high accuracy (86.2%), sensitivity (84.7%), and specificity (85.4%) on training data.
- On testing data, the model demonstrated good performance with accuracy (80.20%), sensitivity (73.68%), and specificity (81.71%).
- The AI model effectively predicted VMI developmental status based on coloring activity performance.
Conclusions:
- A coloring activity combined with AI presents a viable screening tool for VMI.
- This approach offers a promising method for identifying children's VMI developmental status.
- Further research can explore the broader application of this AI-powered screening tool.
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.
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