[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119344-en":3,"doc-seo-119344-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119344,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review","Fetal alcohol spectrum disorders (FASDs) impact at least 0.8% of the global population, yet diagnosis is unusually complex due to diverse physical and neurobehavioral presentations requiring multidisciplinary expertise. To support earlier identification and diagnosis, this narrative review synthesizes how machine learning models are being applied to detect children with FASD or affected by prenatal alcohol exposure. It reviews examples spanning neurobehavioral screening tools and physiologic exposure markers, highlights reported model performance and key limitations, and considers scalability and likely general-population performance.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nMachine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review.  \nPermalink  \n[https://escholarship.org/uc/item/0p81p40b](https://escholarship.org/uc/item/0p81p40b)  \nJournal  \nAlcoholism: Clinical and Experimental Research, 48(4)  \nAuthors  \nSuttie, Michael  \nKable, Julie Mahnke, Amanda et al.  \nPublication Date  \n2024-04-01  \nDOI  \n10.1111/acer.15271  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial License, available at [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nAuthor Manuscr ipt Author Manuscr ipt Author Manuscr ipt Author Manuscript  \n\n|  | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>Alcohol Clin Exp Res (Hoboken). Author manuscript; available in PMC 2025 April 01. |\n| --- | --- |\n\nPublished in final edited form as:  \nAlcohol Clin Exp Res (Hoboken). 2024 April ; 48(4): 585–595. doi:10.1111/acer.15271 .  \nMachine learning approaches in the identification of children affected by prenatal alcohol exposure: A narrative review  \nMichael Suttie, PhD 1,2 , Julie Kable, PhD3 , Amanda H. Mahnke, PhD4 , Gretchen Bandoli, PhD5  \n1.Nuffield Department of Women’s & Reproductive Health, University of Oxford, UK  \n2.Big Data Institute, University of Oxford, UK  \n3.Departments of Psychiatry and Behavioral Science and Pediatrics, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA, 30322, USA  \n4.Department of Neuroscience and Experimental Therapeutics, Texas A&M University School of Medicine, 8447 Riverside Parkway, Bryan, TX 77807, USA  \n5.Department of Pediatrics, University of California San Diego, La Jolla, CA, USA  \nAbstract  \nFetal alcohol spectrum disorders (FASDs) affect at least 0.8% of the population globally. The diagnosis ofFASD is uniquely complex, with a heterogeneous physical and neurobehavioral presentation requiring multidisciplinary expertise for diagnosis. To expand early identification and diagnosis ofFASD, many researchers have begun to incorporate machine learning approaches into FASD research to identify children with FASD or who are affected by prenatal alcohol exposure.  \nThis narrative review highlights these efforts. We first include an introduction to machine learning. We then summarize examples from the literature into neurobehavioral screening tools and physiologic markers of exposure. We discuss individual efforts, including models that classify FASD based on parent-reported neurocognitive or behavioral questionaries, 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 with a broader consideration of the scalability of these approaches and considerations for how these machine learning models, largely developed from clinical samples or highly-exposed birth cohorts, may perform in the general population.  \n“Fetal alcohol spectrum disorders”(FASDs) is a collective term encompassing a range of diagnostic outcomes that result from prenatal alcohol exposure (PAE) . FASDs affect approximately 8 of 1000 people in the global population, with estimates varying drastically by geographic location (Lange et al., 2017a; May et al., 2018) and by ascertainment method (Coles et al., 2022, 2016) . One outcome within FASD is fetal alcohol syndrome (FAS), characterized by central nervous system anomalies, growth deficiency, neurobehavioral deficits, and characteristic facial dysmorphism (Hoyme et al., 2016; Jones, 2011) . While FAS is identifiable by the presence of phenotypic traits, there is a significant variation  \nCorresponding author: Gretchen Bandoli, PhD, 9500 Gilma","cbCaieGyYXa1emZj","https://ap.wps.com/l/cbCaieGyYXa1emZj","pdf",613329,1,21,"English","en",105,"# Introduction\n# Machine learning overview\n# Neurobehavioral screening tools\n## Parent-reported neurocognitive or behavioral questionnaires\n## 3D facial imaging\n## Brain imaging\n## DNA methylation patterns\n## MicroRNA profiles\n## Cardiac orienting response\n## Dysmorphic facial features\n# Model performance and limitations\n# Scalability and general-population considerations","[{\"question\":\"Why is diagnosing fetal alcohol spectrum disorders (FASDs) considered particularly complex?\",\"answer\":\"FASDs show heterogeneous physical and neurobehavioral presentations, making diagnosis difficult and requiring multidisciplinary expertise.\"},{\"question\":\"How does this review categorize machine learning efforts for identification of prenatal alcohol exposure effects?\",\"answer\":\"It groups approaches into neurobehavioral screening tools and physiologic markers of exposure, summarizing example model types from the literature.\"},{\"question\":\"What limitations and scalability concerns are discussed for these machine learning models?\",\"answer\":\"The review highlights limitations of current approaches and considers how models trained on clinical samples or highly exposed cohorts may perform when applied to the general population.\"}]","Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review | 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is diagnosing fetal alcohol spectrum disorders (FASDs) considered particularly complex?","Question",{"text":75,"@type":76},"FASDs show heterogeneous physical and neurobehavioral presentations, making diagnosis difficult and requiring multidisciplinary expertise.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does this review categorize machine learning efforts for identification of prenatal alcohol exposure effects?",{"text":80,"@type":76},"It groups approaches into neurobehavioral screening tools and physiologic markers of exposure, summarizing example model types from the literature.",{"name":82,"@type":73,"acceptedAnswer":83},"What limitations and scalability concerns are discussed for these machine learning models?",{"text":84,"@type":76},"The review highlights limitations of current approaches and considers how models trained on clinical samples or highly exposed cohorts may perform when applied to the general 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