[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127396-en":3,"doc-seo-127396-105":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},127396,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A comprehensive scoping review on machine learning-based fetal echocardiography analysis","Fetal echocardiography supports early identification of congenital heart defects, yet clinical detection misses about half of cases, largely due to image acquisition and interpretation variability. A comprehensive scoping review synthesizes evidence up to July 2023 from major indexing platforms, analyzing 48 selected studies out of 343 found. The review focuses on neural network approaches for fetal heart anatomy classification and segmentation, and summarizes recurring technical analysis categories.","Computers in Biology and Medicine 186 (2025) 109666  \n| A comprehensive scoping review on machine learning-based fetal echocardiography analysis\u003Cbr>Netzahualcoyotl Hernandez-Cruz a ,∗, Olga Pateyb, Clare Teng a, Aris T. Papageorghioub, J. Alison Noble a\u003Cbr>a Institute of Biomedical Engineering, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK\u003Cbr>b Nuffield Department of Women’s & Reproductive Health, University of Oxford, Women’s Centre, John Radcliffe Hospital, Oxford, OX3 9DU, UK |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Technical scoping review Congenital heart defects Fetal echocardiography Image analysis\u003Cbr>Video analysis Machine learning |  | Fetal echocardiography (ultrasound of the fetal heart) plays a vital role in identifying heart defects, allowing clinicians to establish prenatal and postnatal management plans. Machine learning-based methods are emerging to support the automation of fetal echocardiographic analysis; this review presents the findings from a literature review in this area. Searches were queried at leading indexing platforms ACM, IEEE Xplore, PubMed, Scopus, and Web of Science, including papers published until July 2023. In total, 343 papers were found, where 48 papers were selected to compose the detailed review. The reviewed literature presents research on neural network-based methods to identify fetal heart anatomy in classification and segmentation modelling. Thereviewed literature uses five categorical technical analysis terms: attention and saliency, coarse to fine, dilated convolution, generative adversarial networks, and spatio-temporal. This review offers a technical overview for those already working in the field and an introduction to those new to the topic. |  |\n\n1. Introduction  \nCongenital heart defects (CHDs) are the leading cause of neonatal mortality due to birth defects [1], and affect up to 1.2% of live births worldwide [2]. Early detection of CHDs during fetal life is important because it allows an appropriate plan to be made, such as birth in a proper centre equipped to deal with the condition. Evidence suggests that infants diagnosed after birth are less likely to survive (both before and after heart surgery) and are more likely to have adverse long-term neurological outcomes than those diagnosed prenatally [3].  \nAssessment of the fetal heart is an integral part of the routine obstetric scan offered to all pregnant women and usually takes place around 20 weeks as part of the routine anatomical survey [4] using fetal echocardiography. This well-established clinical technique uses prenatal ultrasound imaging to evaluate the fetal heart and nearby connected blood vessels [5,6].  \nFetal echocardiography is non-invasive and allows real-time data acquisition; it is reliable, safe, and inexpensive [7]. It has evolved with the introduction of spectral, colour Doppler [8] and three-dimensional (3D) imaging [9]. However, despite all these advantages and forming a core component of routine fetal ultrasound, it has been estimated that prenatal CHD detection is missed in approximately half of all cases [10]. Fetal echocardiography in clinical settings remains highly human-dependent,  \nrequiring the operator to know the fetal anatomy, excellent hand-eye coordination to acquire informative images [11], and the ability to recognise heart defects (see Fig. 1). Research suggests that the most frequent reasons a CHD is not diagnosed during a second-trimester routine scan are the incorrect acquisition of echocardiographic images or inadequate interpretation of the images acquired [10].  \nTo overcome these deficiencies in image acquisition, detection, and recognition of CHDs, researchers have been investigating the integration of machine learning-based algorithms into fetal ultrasound examination, showing early promising results in clinical image and video analysis, for instance [12–17] (see also Appendix C for d","cbCaipKplanpDRB0","https://ap.wps.com/l/cbCaipKplanpDRB0","pdf",1765765,1,12,"English","en",105,"# Introduction\n## Machine learning integration motivation\n## Prior reviews and scope positioning\n# Method overview\n## Search strategy and study selection\n## Technical focus areas\n# Technical analysis categories\n## Attention and saliency\n## Coarse to fine and dilated convolution\n## Generative adversarial networks\n## Spatio-temporal methods","[{\"question\":\"Why is fetal echocardiography important in congenital heart defect detection?\",\"answer\":\"It enables prenatal ultrasound assessment of the fetal heart, supporting early planning for prenatal and postnatal management. Earlier identification improves survival chances and reduces adverse long-term neurological outcomes.\"},{\"question\":\"What problem motivates applying machine learning to fetal echocardiography?\",\"answer\":\"Detection misses are linked to incorrect image acquisition and inadequate interpretation. Machine learning aims to automate and assist analysis of echocardiographic images and videos.\"},{\"question\":\"Which machine learning technical areas does the review emphasize?\",\"answer\":\"The review highlights categories such as attention and saliency, coarse to fine, dilated convolution, generative adversarial networks, and spatio-temporal approaches.\"}]","A comprehensive scoping review on machine learning-based fetal echocardiography analysis | PDF",1785938674,30,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-comprehensive-scoping-review-on-machine-learning-based-fetal-echocardiography-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@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/a-comprehensive-scoping-review-on-machine-learning-based-fetal-echocardiography-analysis/127396/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is fetal echocardiography important in congenital heart defect detection?","Question",{"text":75,"@type":76},"It enables prenatal ultrasound assessment of the fetal heart, supporting early planning for prenatal and postnatal management. Earlier identification improves survival chances and reduces adverse long-term neurological outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem motivates applying machine learning to fetal echocardiography?",{"text":80,"@type":76},"Detection misses are linked to incorrect image acquisition and inadequate interpretation. Machine learning aims to automate and assist analysis of echocardiographic images and videos.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning technical areas does the review emphasize?",{"text":84,"@type":76},"The review highlights categories such as attention and saliency, coarse to fine, dilated convolution, generative adversarial networks, and spatio-temporal approaches.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]