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Voice as a predictive signal: protocol for a scoping review of AI in early childhood development
Mehrdad Amir-Behghadami1,2,3, Seifollah Heidarabadi1,2
1Student Research Committee (SRC), Tabriz University of Medical Sciences, Tabriz, East Azerbaijan Province, Iran (the Islamic Republic of) seifollahheidarabady@gmail.com behghadami.m@gmail.com.
Insights
Artificial intelligence (AI) and machine learning (ML) voice biomarkers can aid early detection of developmental delays in children aged 0-5 years. This review synthesizes evidence on AI-driven voice analysis for monitoring and managing early childhood development outcomes.
Area of Science:
- Pediatric Health Informatics
- Developmental Pediatrics
- Artificial Intelligence in Healthcare
Background:
- Current early childhood development (ECD) assessments face accessibility, cost, and invasiveness barriers.
- Voice analysis offers a non-invasive digital biomarker for assessing children aged 0-5 years.
- Artificial intelligence (AI) and machine learning (ML) can analyze vocal features for developmental insights.
Purpose of the Study:
- To synthesize evidence on AI-driven voice biomarkers for early detection, monitoring, and management of ECD outcomes.
- To explore the application of voice as a predictive signal in healthy, at-risk, or impaired children under five.
- To inform the integration of AI in pediatric health informatics for equitable child development support.
Main Methods:
- Scoping review protocol adhering to PRISMA-ScR and Arksey and O'Malley framework.
- Population, Concept, Context (PCC) eligibility criteria for children aged 0-5 years, AI/ML voice biomarkers, and ECD outcomes.
- Comprehensive literature searches across multiple databases (PubMed, Scopus, Web of Science, etc.) and grey literature, from January 2015 onwards.
Main Results:
- Data extraction focused on clinical applications, recording protocols, acoustic features, AI models, demographics, and implementation factors.
- Two independent reviewers screened titles, abstracts, and full texts.
- Results will be presented narratively with supporting tables, charts, and figures.
Conclusions:
- AI-driven voice biomarkers show promise for non-invasive ECD assessment.
- Further research is needed to optimize AI models and clinical implementation for equitable child development.
- This review will guide the integration of voice biomarkers in pediatric healthcare.
Introduction:
Early childhood development (ECD) interventions support children aged 0-5 years, including those typically developing, at risk of delays or diagnosed with motor, cognitive, language or social-emotional disorders. Current assessments face barriers like limited access, high costs, intermittent evaluations and invasive methods. Voice offers a non-invasive digital biomarker, with artificial intelligence (AI) and machine learning (ML) enabling analysis of vocal features linked to developmental trajectories. This scoping review protocol synthesises evidence on AI-driven voice biomarkers for early detection, monitoring and management of ECD outcomes in healthy, at-risk or impaired children under five.
Methods And Analysis:
This scoping review protocol adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and the Arksey and O'Malley framework, enhanced by Joanna Briggs Institute recommendations. Eligibility criteria follow the Population, Concept and Context framework: population (children 0-5 years), concept (AI/ML voice biomarker analysis), context (early detection, monitoring, management of developmental outcomes). Comprehensive searches target PubMed/MEDLINE, Scopus, Web of Science, Embase, IEEE Xplore, CINAHL and grey literature sources for peer-reviewed English/Persian articles from January 2015 onwards. Two independent reviewers screen titles/abstracts/full texts and extract data on clinical applications, recording protocols, acoustic features, AI models, demographics and implementation factors. Discrepancies were resolved via discussion or a third reviewer. Results were presented narratively with tables, charts and figures addressing research questions on voice as a predictive signal.
Ethics And Dissemination:
The Research Ethics Committee of Tabriz University of Medical Sciences approved this protocol, confirming ethical compliance absent patient involvement. Findings were disseminated via peer-reviewed journals, conferences and institutional seminars to inform AI integration in paediatric health informatics for equitable child development support.
Systematic Review Registration:
Not registered.
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