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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\u2010reported 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.<\/jats:p>","DOI":"10.1111\/acer.15271","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T02:49:40Z","timestamp":1706842180000},"page":"585-595","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review"],"prefix":"10.1111","volume":"48","author":[{"given":"Michael","family":"Suttie","sequence":"first","affiliation":[{"name":"Nuffield Department of Women's and Reproductive Health University of Oxford  Oxford UK"},{"name":"Big Data Institute University of Oxford  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5008-0887","authenticated-orcid":false,"given":"Julie","family":"Kable","sequence":"additional","affiliation":[{"name":"Departments of Psychiatry and Behavioral Science and Pediatrics Emory University School of Medicine  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanda H.","family":"Mahnke","sequence":"additional","affiliation":[{"name":"Department of Neuroscience and Experimental Therapeutics Texas A&amp;M University School of Medicine  Bryan Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2167-8921","authenticated-orcid":false,"given":"Gretchen","family":"Bandoli","sequence":"additional","affiliation":[{"name":"Department of Pediatrics University of California San Diego  La Jolla California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,2]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3941049"},{"key":"e_1_2_9_3_1","first-page":"56","article-title":"Focus on: biomarkers of fetal alcohol exposure and fetal alcohol effects","volume":"34","author":"Bakhireva L.N.","year":"2011","journal-title":"Alcohol Research and Health"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0165081"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/acer.14966"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/acer.15233"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-2002-36735"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/acer.14749"},{"key":"e_1_2_9_9_1","first-page":"2222","article-title":"What is machine learning? 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