Automated Approaches to Screening Developmental Language Disorder: A Comprehensive Review and Future Prospects.

Abstract

Purpose: This study examines existing automatic screening methods for developmental language disorder (DLD), a neurodevelopmental language deficit without known biomedical etiologies, focusing on languages, data sets, extracted features, performance metrics, and classification methods. Additionally, it summarizes the strengths and weaknesses of current systems and explores future research opportunities and challenges.

Method: We conducted a systematic review, searching PubMed, Web of Science, Scopus, and PsycINFO for articles published in English before March 2024. We included studies that developed automated screening systems to classify DLD cases among children.

Results: A total of 23 studies were thoroughly reviewed. We found that automatic screening models for DLD focused on five languages, namely, Czech, Italian, Mandarin, Spanish, and English, with various data sets employed. Most studies identified and used acoustic, textural, and combination of speech features and nonspeech features for model development. Traditional machine learning, artificial neural networks, convolutional neural networks, long short-term memory, and non-machine-learning classification methods were employed in model training. The need for larger, multilingual data sets and improved system sensitivity is noted. Future research opportunities include exploring the integration of combined features and algorithms; implementing new algorithms; and considering variations in age, gender, severity, and comorbidity differences in DLD.

Conclusion: This systematic review of existing automatic screening methods for DLD highlights significant advancements and suggests potential areas in future research on automatic DLD screening.