[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121651-en":3,"doc-seo-121651-105":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},121651,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",7,"Healthcare","Machine learning for accurate estimation of fetal gestational age based on ultrasound images - research paper","Accurate gestational age estimation underpins high-quality obstetric care and supports clinical decisions across pregnancy and at population level. When last menstrual period dates are unknown or unreliable, ultrasound size measurements are standard, yet late-pregnancy dating is limited by fetal growth variability and can misclassify small or large for gestational age. This work applies state-of-the-art machine learning to estimate gestational age from image analysis of standard ultrasound planes only, using training, internal validation, and external validation datasets.","eCommons@AKU  \n\n| Obstetrics and Gynaecology, East Africa | Medical College, East Africa |\n| --- | --- |\n\n3-2023  \nMachine learning for accurate estimation of fetal gestational age based on ultrasound images  \nLok Hin Lee  \nElizabeth Bradburn Rachel Craik Mohammad Yaqub  \nShane A. Norris  \nSee next page for additional authors  \nFollow this and additional works at: [https://ecommons.aku.edu/eastafrica_fhs_mc_obstet_gynaecol](https://ecommons.aku.edu/eastafrica_fhs_mc_obstet_gynaecol)  \n Part of the Obstetrics and Gynecology Commons  \nAuthors  \nLok Hin Lee, Elizabeth Bradburn, Rachel Craik, Mohammad Yaqub, Shane A. Norris, Leila Cheikh Ismail, Eric O. Ohuma, Fernando C. Barros, Maria Carvalho, Shama Munim, and Zulfiqar Ahmed Bhutta  \nARTICLE OPEN  \n[www.nature.com/npjdigitalmed](www.nature.com/npjdigitalmed)  \nMachine learning for accurate estimation of fetal gestational age based on ultrasound images  \nLok Hin Lee 1,19, Elizabeth Bradburn 2,19, Rachel Craik 2, Mohammad Yaqub3, Shane A. Norris4, Leila Cheikh Ismail5, Eric O. Ohuma2,6, Fernando C. Barros7,8, Ann Lambert2, Maria Carvalho9, Yasmin A. Jaffer10, Michael Gravett11, Manorama Purwar12, Qingqing Wu13, Enrico Bertino14, Shama Munim15, Aung Myat Min16, Zulfiqar Bhutta15,17, Jose Villar2,18, Stephen H. Kennedy2,18,  \nJ. Alison Noble 1,20 and Aris T. Papageorghiou 2,18,20 ✉  \n\n|  | Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least\u003Cbr>±2 weeks’ gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length) . We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9–3.2) and 4.3 (95% CI, 4.1–4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages Our method for dating the pregnancy in the |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| .\u003Cbr>second and third trimesters is, therefore, more accurate than published methods. |  |  |\n|  | npj Digital Medicine (2023)6:36; [https://doi.org/10.1038/s41746-023-00774-2](https://doi.org/10.1038/s41746-023-00774-2) |  |\n|  |  |  |\n\nINTRODUCTION  \nFailure to estimate gestational age (GA) accurately remains an important barrier to the provision of evidence-based pregnancy care in many low- and middle-income countries (LMICs)1. An accurate estimate of GA is crucial to inform decision-making at the individual level. It is also essential at population level to measure causes of infant morbidity and mortality, such as preterm birth and small for GA (SGA)2 - information that is needed for public health strategies to improve health outcomes.  \nCurrently, ultrasound measurement of the fetal crown rump length (CR","cbCaibExi7BYhMHY","https://ap.wps.com/l/cbCaibExi7BYhMHY","pdf",2221451,1,13,"English","en",105,"# Introduction\n## Clinical need and limitations of current dating methods\n## Proposed machine learning approach","[{\"question\":\"Why is accurate gestational age estimation important in obstetric care?\",\"answer\":\"It guides individual clinical decisions throughout pregnancy and enables population-level measurement of outcomes such as preterm birth and small for gestational age.\"},{\"question\":\"What limits gestational age estimation in the second and third trimesters with standard ultrasound biometry?\",\"answer\":\"Late-pregnancy ultrasound dating is affected by increasing fetal size variation and growth abnormalities, which widen the prediction interval and can bias estimates for SGA and LGA fetuses.\"},{\"question\":\"How does the proposed machine learning method estimate gestational age?\",\"answer\":\"It estimates gestational age using only image analysis from standard ultrasound planes, without relying on any measurement inputs, and is evaluated with internal and external validation while blinded to ground truth.\"}]","Machine learning for accurate estimation of fetal gestational age based on ultrasound images - research paper | PDF",1785805948,33,{"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},"machine-learning-for-accurate-estimation-of-fetal-gestational-age-based-on-ultrasound-images-research-paper","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-accurate-estimation-of-fetal-gestational-age-based-on-ultrasound-images-research-paper/121651/",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","2026-08-04",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 accurate gestational age estimation important in obstetric care?","Question",{"text":75,"@type":76},"It guides individual clinical decisions throughout pregnancy and enables population-level measurement of outcomes such as preterm birth and small for gestational age.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limits gestational age estimation in the second and third trimesters with standard ultrasound biometry?",{"text":80,"@type":76},"Late-pregnancy ultrasound dating is affected by increasing fetal size variation and growth abnormalities, which widen the prediction interval and can bias estimates for SGA and LGA fetuses.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed machine learning method estimate gestational age?",{"text":84,"@type":76},"It estimates gestational age using only image analysis from standard ultrasound planes, without relying on any measurement inputs, and is evaluated with internal and external validation while blinded to ground truth.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]