ReviewAlcohol, clinical & experimental research2024
Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review.
Review in Alcohol, clinical & experimental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- What Do We Know About Alcohol Consumption During Pregnancy and Fetal Alcohol Spectrum Disorder in Nigeria? A Narrative Review.Health science reports · 2026Article
- Does a Biochemical Approach Facilitate the Diagnosis of Prenatal Alcohol Exposure and Fetal Alcohol Spectrum Disorder in Neonatal Period?International journal of molecular sciences · 2026Review
- From microbiome dysbiosis to virtual reality and nanomedicine: a multidisciplinary roadmap for pediatric glaucoma care.Annals of medicine and surgery (2012) · 2025Article
- Need for international consensus on diagnostic criteria for fetal alcohol spectrum disorders (FASD): Commentary on Myers et al., "Comparing rates of agreement between different diagnostic criteria for fetal alcohol spectrum disorder: A systematic review".Alcohol, clinical & experimental research · 2025Article
- Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.Frontiers in neuroscience · 2024Article
- Machine learning-driven blood biomarker profiling and EGCG intervention in fetal alcohol spectrum disorder.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Fetal alcohol spectrum disorders (FASDs) affect at least 0.8% of the population globally. The diagnosis of FASD is uniquely complex, with a heterogeneous physical and neurobehavioral presentation that requires multidisciplinary expertise for diagnosis. Many researchers have begun to incorporate machine learning approaches into FASD research to identify children who are affected by prenatal alcohol exposure, including those with FASD. This narrative review highlights these efforts. Following an introduction to machine learning, we summarize examples from the literature of neurobehavioral screening tools and physiologic markers of exposure. We discuss individual efforts, including models that classify FASD based on parent-reported neurocognitive or behavioral questionnaires, 3D facial imaging, brain imaging, DNA methylation patterns, microRNA profiles, cardiac orienting response, and dysmorphic facial features. We highlight model performance and discuss the limitations of these approaches. We conclude by considering the scalability of these approaches and how these machine learning models, largely developed from clinical samples or highly exposed birth cohorts, may perform in the general population.
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Registered trials
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