โก Quick Summary
This study developed an AI-based screening app to identify children with Developmental Language Disorder (DLD) by analyzing key linguistic markers. The app demonstrated high concordance with clinical diagnoses, showcasing its potential as a reliable tool in under-resourced settings.
๐ Key Details
- ๐ถ Participants: 30 children aged 7-10 (15 with DLD, 15 typically developing)
- ๐ง Assessments: Vocabulary production, morphosyntactic abilities, sentence repetition
- โ๏ธ Technology: Random forest classifier trained on real and synthetic datasets
- ๐ Validation: High concordance with clinical diagnoses by speech-language pathologists
๐ Key Takeaways
- ๐ Linguistic markers are crucial for distinguishing children with DLD from their peers.
- ๐ก AI-driven solutions can enhance the identification process for DLD.
- ๐ Synthetic data augmentation improves the robustness of AI models.
- ๐ฅ The app’s interpretability reduces subjectivity in diagnosis.
- โณ Time efficiency in the diagnostic process is significantly improved.
- ๐ Potential for scalability in under-resourced settings.
- ๐ Bayesian analyses provided strong evidence for significant group differences.
๐ Background
Developmental Language Disorder (DLD) affects a significant number of children, leading to challenges in communication and learning. Traditional diagnostic methods can be subjective and time-consuming, often requiring extensive assessments by trained professionals. The integration of artificial intelligence into the diagnostic process offers a promising avenue for enhancing accuracy and efficiency.
๐๏ธ Study
The study involved a cohort of 30 children, aged 7-10, who underwent a comprehensive verbal assessment battery. This included evaluations of vocabulary production, morphosyntactic abilities, and sentence repetition. A random forest classifier was then trained using both real and synthetically generated datasets to create an online screening app aimed at identifying children with DLD.
๐ Results
The results indicated that the screening app achieved high concordance with clinical diagnoses made by speech-language pathologists. Bayesian analyses revealed significant group differences across all linguistic measures, underscoring the app’s reliability in identifying children with DLD.
๐ Impact and Implications
The findings from this study highlight the potential of AI-driven screening tools to transform the identification of DLD. By providing a scalable and interpretable solution, the app can significantly reduce the time and subjectivity involved in the diagnostic process, particularly in under-resourced settings. This innovation could lead to earlier interventions and improved outcomes for children with DLD.
๐ฎ Conclusion
This study demonstrates the feasibility of using AI to enhance the identification of Developmental Language Disorder. The development of an explainable screening app not only supports the diagnostic value of specific linguistic indicators but also paves the way for future research into AI applications in pediatric language disorders. The future of DLD identification looks promising with these technological advancements!
๐ฌ Your comments
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Enhancing Developmental Language Disorder Identification with Artificial Intelligence: Development of an Explainable Screening App Using Real and Synthetic Data.
Abstract
PURPOSE: This 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.
METHOD: Thirty 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.
RESULTS: Bayesian 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.
CONCLUSION: These 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.
Author: [‘Georgiou GP’]
Journal: J Autism Dev Disord
Citation: Georgiou GP. Enhancing Developmental Language Disorder Identification with Artificial Intelligence: Development of an Explainable Screening App Using Real and Synthetic Data. Enhancing Developmental Language Disorder Identification with Artificial Intelligence: Development of an Explainable Screening App Using Real and Synthetic Data. 2025; (unknown volume):(unknown pages). doi: 10.1007/s10803-025-07176-1