[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116961-en":3,"doc-seo-116961-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},116961,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine learning and disease prediction in obstetrics - Article review","Machine learning technologies and artificial intelligence tools are rapidly reshaping obstetric and maternity care. Predictive systems increasingly draw on electronic health records, diagnostic imaging, and digital devices to support fetal well-being and to predict, screen, or diagnose obstetric diseases. This review synthesizes recent machine learning methods, model-building algorithms, and evaluation challenges for conditions including gestational diabetes, pre-eclampsia, preterm birth, and fetal growth restriction. It also addresses intelligent imaging tools using ultrasound and magnetic resonance imaging, prenatal sequencing to reduce preterm risk, and early complication detection to strengthen safety in intrapartum care.","Current Research in Physiology 6 (2023) 100099  \nContents lists available at ScienceDirect  \nCurrent Research in Physiology  \njournal [homepage: www.sciencedirect.com/journal/current-research-in-physiology](homepage: www.sciencedirect.com/journal/current-research-in-physiology)  \n| Machine learning and disease prediction in obstetrics |  |  |  |\n| --- | --- | --- | --- |\n| Zara Arain a, Stamatina Iliodromitib, Gregory Slabaugh c, Anna L. David d, Tina T. Chowdhury a, *\u003Cbr>a Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK b Women’s Health Research Unit, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK c Digital Environment Research Institute, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 1HH, UK d Elizabeth Garrett Anderson Institute for Women’s Health, University College London, Medical School Building, Huntley Street, London, WC1E 6AU, UK |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Obstetrics Gestational diabetes Pre-eclampsia\u003Cbr>Preterm birth\u003Cbr>Machine learning Echocardiography Cardiotocography Magnetic resonance imaging Ultrasound |  | Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice. |  |\n\n1. Introduction  \nThe digitisation of health records along with advancements in biomedical imaging and medical devices has led to an increase in clinical, biological and imaging data. Within these vast data sets of growing complexity and scale, lies the opportunity to understand challenging disease processes in obstetrics and maternity care where timely interventions can change the outcome for both mothers and babies. New approaches in computer science and statistics are required to identify actionable insights within these clinical conditions (de Marvao et al., 2020). Machine learning is a branch of artificial intelligence that uses computer algorithms to identify patterns within large raw datasets, acquire knowledge and apply this to different tasks (Ahn and Lee, 2022; Dhombres, 2022). A single machine learning model could analyse more data than a clinician would encounter over the duration of the individual’s career. The multi-disciplinary field of computer science, engineering and maternal-fetal medicine is increasingly enabling researchers to apply machine learning tools that are transforming obstetrics and maternity care.  \n1.1. Differences between machine learning and deep learning platforms  \nArtificial intelligence refers to computer systems which perform tasks that typically require human intelligence. These systems mimic human behaviour and can be programmed to complete a","cbCailr8pPMx8R1S","https://ap.wps.com/l/cbCailr8pPMx8R1S","pdf",2374939,1,9,"English","en",105,"# Introduction\n## Differences between machine learning and deep learning platforms\n# Predictive tools and clinical challenges\n## Fetal well-being and disease prediction\n## Imaging-based intelligent diagnostics\n# Prenatal diagnosis and risk reduction\n## Machine learning for intrapartum safety and early detection","[{\"question\":\"How does machine learning differ from deep learning in obstetric applications?\",\"answer\":\"Deep learning is a subset of artificial intelligence, while machine learning can learn from experience without specific programming. Machine learning approaches can also manage multi-dimensional datasets with many variables compared with traditional statistical methods.\"},{\"question\":\"What obstetric conditions does the review focus on for machine learning prediction?\",\"answer\":\"The review addresses predictive and diagnostic tasks for gestational diabetes, pre-eclampsia, preterm birth, and fetal growth restriction, with an emphasis on supporting fetal well-being.\"},{\"question\":\"Which data sources and imaging modalities are used to build predictive models?\",\"answer\":\"Predictive tools use electronic health records, diagnostic imaging, and digital devices. The review specifically discusses intelligent diagnostic imaging approaches using ultrasound and magnetic resonance imaging, including prenatal imaging sequencing.\"}]","Machine learning and disease prediction in obstetrics - Article review | PDF",1785672868,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-and-disease-prediction-in-obstetrics-article-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-disease-prediction-in-obstetrics-article-review/116961/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does machine learning differ from deep learning in obstetric applications?","Question",{"text":76,"@type":77},"Deep learning is a subset of artificial intelligence, while machine learning can learn from experience without specific programming. Machine learning approaches can also manage multi-dimensional datasets with many variables compared with traditional statistical methods.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What obstetric conditions does the review focus on for machine learning prediction?",{"text":81,"@type":77},"The review addresses predictive and diagnostic tasks for gestational diabetes, pre-eclampsia, preterm birth, and fetal growth restriction, with an emphasis on supporting fetal well-being.",{"name":83,"@type":74,"acceptedAnswer":84},"Which data sources and imaging modalities are used to build predictive models?",{"text":85,"@type":77},"Predictive tools use electronic health records, diagnostic imaging, and digital devices. 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