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Journal Article
Interpretable Autism Screening via Object-Based Analysis of Child-Drawn Sketches
- Authors
- Tae-Gyun Lee, Jang-Hee Yoo
- Issue Date
- 2025-12
- Citation
- IEEE Access, v.13, pp.211138-211150
- ISSN
- 2169-3536
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- Developmental assessment methods for children largely rely on subjective manual scoring, which leads to limitations in interpretability, consistency, and scalability. For high-stakes applications in clinical and educational environments, high accuracy and transparency are essential in decision-making, which underscores the growing importance of explainable artificial intelligence techniques for automated developmental assessment. To address these challenges, we propose an explainable deep learning framework for automated developmental assessment based on freehand portraits drawn by children. The proposed system integrates clinically evaluated criteria directly into the design of a computational model to ensure both accuracy and interpretability. Focusing specifically on unstructured freehand portrait sketches, the framework introduces an object-based contribution analysis that segments each sketch into semantically meaningful parts such as eyes, nose, ears, and torso, for localized contribution scoring. A customized vision transformer architecture equipped with relative position encoding and safe layer normalization was utilized to model inter-object dependencies and extract robust features. Contribution scores were computed using layer-wise relevance propagation and visualized via heatmaps and bar plots, to allow clinicians to trace the rationale behind the predictions. The results of an extensive evaluation demonstrated that this method yields high classification accuracy in distinguishing between autism spectrum disorder and typical development, and provides rule-aligned explanations that mirror clinical reasoning. The findings of this study provide a foundation for future real-world deployment of early screening, special education, and pediatric neuropsychological applications.
- KSP Keywords
- Artificial intelligence techniques, Assessment method, Autism screening, Computational Model, Contribution analysis, Decision-making, Deep learning framework, Extensive evaluation, High accuracy, Object-based analysis, Real-world deployment
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
(CC BY NC ND)
