Researchers in Vietnam have utilized machine learning to improve the identification of autism risk in toddlers, according to a recent study. The study incorporated the modified Checklist for Autism in Toddlers, Revised (M-CHAT-R), alongside perinatal predictors, to refine autism risk stratification methods. This approach aimed to provide a more detailed understanding of early childhood autism risk factors.
The research team applied advanced machine learning techniques to analyze data from M-CHAT-R screenings and various perinatal indicators. By integrating these elements, they sought to enhance the accuracy of identifying children at higher risk for autism spectrum disorder during their formative years. The findings highlight the potential role of artificial intelligence in supporting early detection efforts and improving outcomes for children with developmental challenges.
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Source: GO-AI-ne1
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Date: January 24, 2026
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