ArticleJournal of autism and developmental disorders2025
Enhancing Developmental Language Disorder Identification with Artificial Intelligence: Development of an Explainable Screening App Using Real and Synthetic Data.
Article in Journal of autism and developmental disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis study aims to evaluate key linguistic markers for distinguishing children with developmental language disorder (DLD) from their typically developing (TD) peers and to develop an artificial intelligence (AI)-based, explainable screening app.
methodThirty children aged 7-10 (15 with DLD and 15 TD) completed a verbal assessment battery measuring vocabulary production, morphosyntactic abilities, and sentence repetition. Based on these data, a random forest classifier was trained on synthetically generated datasets to develop an online, explainable screening app.
resultsBayesian analyses provided strong evidence for significant group differences across all three linguistic measures. The screening app, when validated on unseen cases, demonstrated high concordance with clinical diagnoses made by speech-language pathologists, indicating its reliability in identifying children with DLD.
conclusionThese findings support the diagnostic value of specific linguistic indicators in identifying DLD and demonstrate the feasibility of an AI-driven screening solution. The app's interpretability and scalability offer practical advantages for detection, particularly in under-resourced settings, by reducing subjectivity and time demands in the diagnostic process. Moreover, this study highlights the potential of synthetic data augmentation to overcome limitations associated with small clinical datasets, thereby enhancing the robustness and generalizability of AI-based screening apps.
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