Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review.

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.