Application of machine learning in early childhood development research: a scoping review.

Background: Early childhood development (ECD) lays the foundation for lifelong health, academic success and social well-being, yet over 250 million children in low- and middle-income countries are at risk of not reaching their developmental potential. Traditional measures fail to fully capture the risks associated with a child's development outcomes. Artificial intelligence techniques, particularly machine learning (ML), offer an innovative approach by analysing complex datasets to detect subtle developmental patterns.

Objective: To map the existing literature on the use of ML in ECD research, including its geographical distribution, to identify research gaps and inform future directions. The review focuses on applied ML techniques, data types, feature sets, outcomes, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and reported limitations.

Design: Scoping review using the Arksey and O'Malley framework with enhancements by Levac DATA SOURCES: A systematic search was conducted on 16 June 2024 across PubMed, Web of Science, IEEE Xplore and PsycINFO, supplemented by grey literature (OpenGrey) and reference hand-searching. No publication date limits were applied.

Eligibility Criteria: Included studies applied ML or its variants (eg, deep learning (DL), natural language processing) to developmental outcomes in children aged 0-8 years. Studies were in English and addressed cognitive, language, motor or social-emotional development. Excluded were studies focusing on robotics; neurodevelopmental disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder and communication disorders; disease or medical conditions; and review articles.

Data Extraction And Charting: Three reviewers independently extracted data using a structured MS Excel template, covering study ML techniques, data types, feature sets, outcomes, outcome measures, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and limitations. A narrative synthesis was conducted, supported by descriptive statistics and visualisations.

Results: Of the 759 articles retrieved, 27 met the inclusion criteria. Most studies (78%) originated from high-income countries, with none from sub-Saharan Africa. Supervised ML classifiers (40.7%) and DL techniques (22.2%) were the most used approaches. Cognitive development was the most frequently targeted outcome (33.3%), often measured using the Bayley Scales of Infant and Toddler Development-III (33.3%). Data types varied, with image, video and sensor-based data being most prevalent. Key predictive features were grouped into six categories: brain features; anthropometric and clinical/biological markers; socio-demographic and environmental factors; medical history and nutritional indicators; linguistic and expressive features; and motor indicators. Most studies (74.1%) focused solely on prediction, with the majority conducting predictions at age 2 years and above. Only 41% of studies employed explainability methods, and validation strategies varied widely. Few studies (7.4%) conducted external validation, and only one had progressed to a clinical trial. Common limitations included small sample sizes, lack of external validation and imbalanced datasets.

Conclusion: There is growing interest in using ML for ECD research, but current research lacks geographical diversity, external validation, explainability and practical implementation. Future work should focus on developing inclusive, interpretable and externally validated models that are integrated into real-world implementation.