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<p>Diagnosing autism spectrum disorder (ASD) conventionally demands significant time and resources. Language deficits are key markers of ASD, particularly in constructing narratives. This study leverages computational models to analyze story book narratives from seven children with ASD and 16 typically-developing (TD) peers. By transcribing and training models on limited data using augmentation techniques, our best model achieved over 90% accuracy, sensitivity, and specificity-outperforming previous models by 20% in ASD detection. This research showcases the efficacy of our approach in efficiently assessing language abilities and identifying ASD tendencies. The method holds promise for enhancing diagnostic efficiency and providing comprehensive language evaluations to support children with ASD and their caregivers.</p>

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Using Computational Models to Detect Autistic Tendencies for Children from their Story Book Narratives

  • 期刊论文

  • 发表日期：

    2025年1月7日

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  • Ruihan Sun,
  • Jasin Wong,
  • Eva E. Chen,
  • Arbee L. P. Chen

摘要

Diagnosing autism spectrum disorder (ASD) conventionally demands significant time and resources. Language deficits are key markers of ASD, particularly in constructing narratives. This study leverages computational models to analyze story book narratives from seven children with ASD and 16 typically-developing (TD) peers. By transcribing and training models on limited data using augmentation techniques, our best model achieved over 90% accuracy, sensitivity, and specificity-outperforming previous models by 20% in ASD detection. This research showcases the efficacy of our approach in efficiently assessing language abilities and identifying ASD tendencies. The method holds promise for enhancing diagnostic efficiency and providing comprehensive language evaluations to support children with ASD and their caregivers.