{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:49:02Z","timestamp":1780915742913,"version":"3.54.1"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T00:00:00Z","timestamp":1736553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T00:00:00Z","timestamp":1736553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"RCUK | Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["WT203148\/Z\/16\/Z"],"award-info":[{"award-number":["WT203148\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"RCUK | Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["WT203148\/Z\/16\/Z"],"award-info":[{"award-number":["WT203148\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"RCUK | Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["WT203148\/Z\/16\/Z"],"award-info":[{"award-number":["WT203148\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"RCUK | Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["WT203148\/Z\/16\/Z"],"award-info":[{"award-number":["WT203148\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"RCUK | Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["WT203148\/Z\/16\/Z"],"award-info":[{"award-number":["WT203148\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"DH | National Institute for Health Research","doi-asserted-by":"publisher","award":["NIHR301448"],"award-info":[{"award-number":["NIHR301448"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by aggregating automatically extracted biometrics from every frame across an entire scan, with no need for operator intervention. We use a neural network to classify each frame of an ultrasound video recording. We then measure fetal biometrics in every frame where appropriate anatomy is visible. We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers. We performed a retrospective experiment on 1457 recordings (comprising 48 million frames) of 20-week ultrasound scans, estimated fetal biometrics in those scans and compared our estimates to real-time manual measurements. Our method achieves human-level performance in estimating fetal biometrics and estimates well-calibrated credible intervals for the true biometric value.<\/jats:p>","DOI":"10.1038\/s41746-024-01406-z","type":"journal-article","created":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T15:35:59Z","timestamp":1736609759000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans"],"prefix":"10.1038","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7951-6514","authenticated-orcid":false,"given":"Lorenzo","family":"Venturini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Budd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfonso","family":"Farruggia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Wright","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4754-0322","authenticated-orcid":false,"given":"Jacqueline","family":"Matthew","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas G.","family":"Day","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7813-5023","authenticated-orcid":false,"given":"Bernhard","family":"Kainz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Razavi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2690-5495","authenticated-orcid":false,"given":"Jo V.","family":"Hajnal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,11]]},"reference":[{"key":"1406_CR1","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1002\/uog.20272","volume":"53","author":"LJ Salomon","year":"2019","unstructured":"Salomon, L. J. et al. Isuog practice guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound Obstet. Gynecol. 53, 715\u2013723 (2019).","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"1406_CR2","unstructured":"England, N. H. S. Fetal Anomaly Screening Programme Handbook. https:\/\/www.gov.uk\/government\/publications\/fetal-anomaly-screening-programme-handbook (2021)."},{"key":"1406_CR3","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1002\/uog.24888","volume":"59","author":"LJ Salomon","year":"2022","unstructured":"Salomon, L. J. et al. Isuog practice guidelines (updated): performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet. Gynecol. 59, 840\u2013856 (2022).","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"1406_CR4","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1002\/uog.21929","volume":"55","author":"L Drukker","year":"2020","unstructured":"Drukker, L., Droste, R., Chatelain, P., Noble, J. A. & Papageorghiou, A. T. Expected-value bias in routine third-trimester growth scans. Ultrasound Obstet. Gynecol. 55, 375\u2013382 (2020).","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"1406_CR5","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1002\/uog.10082","volume":"39","author":"I Sarris","year":"2012","unstructured":"Sarris, I. et al. Intra- and interobserver variability in fetal ultrasound measurements. Ultrasound Obstet. Gynecol. 39, 266\u2013273 (2012).","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"1406_CR6","unstructured":"European Platform on Rare Diseases Registration. Prenatal detection rates charts and tables. https:\/\/eu-rd-platform.jrc.ec.europa.eu\/eurocat\/eurocat-data\/prenatal-screening-and-diagnosis_en."},{"key":"1406_CR7","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1109\/TMI.2017.2712367","volume":"36","author":"CF Baumgartner","year":"2017","unstructured":"Baumgartner, C. F. et al. Sononet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans. Med. Imaging 36, 2204\u20132215 (2017).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1406_CR8","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-319-24553-9_62","volume":"9349","author":"H Chen","year":"2015","unstructured":"Chen, H. et al. Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. Lect. Notes Comput. Sci. 9349, 507\u2013514 (2015).","journal-title":"Lect. Notes Comput. Sci."},{"key":"1406_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-67076-5","volume":"10","author":"XP Burgos-Artizzu","year":"2020","unstructured":"Burgos-Artizzu, X. P. et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci. Rep. 10, 1\u201312 (2020).","journal-title":"Sci. Rep."},{"key":"1406_CR10","doi-asserted-by":"publisher","first-page":"7771","DOI":"10.1109\/TII.2021.3069470","volume":"17","author":"B Pu","year":"2021","unstructured":"Pu, B., Li, K., Li, S. & Zhu, N. Automatic fetal ultrasound standard plane recognition based on deep learning and IIoT. IEEE Trans. Ind. Inform. 17, 7771\u20137780 (2021).","journal-title":"IEEE Trans. Ind. Inform."},{"key":"1406_CR11","doi-asserted-by":"crossref","unstructured":"Sinclair, M. et al. Human-level performance on automatic head biometrics in fetal ultrasound using fully convolutional neural networks. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2018 July, pp. 714\u2013717 (2018).","DOI":"10.1109\/EMBC.2018.8512278"},{"key":"1406_CR12","doi-asserted-by":"publisher","first-page":"S312","DOI":"10.1016\/j.ajog.2020.12.512","volume":"224","author":"M Yaqub","year":"2021","unstructured":"Yaqub, M. et al. 491 scannav audit: an AI-powered screening assistant for fetal anatomical ultrasound. Am. J. Obstet. Gynecol. 224, S312 (2021).","journal-title":"Am. J. Obstet. Gynecol."},{"key":"1406_CR13","first-page":"257","volume":"1517 CCIS","author":"S Plotka","year":"2021","unstructured":"Plotka, S. et al. FetalNet: multi-task deep learning framework for fetal ultrasound biometric measurements. Commun. Comput. Inf. Sci. 1517 CCIS, 257\u2013265 (2021).","journal-title":"Commun. Comput. Inf. Sci."},{"key":"1406_CR14","doi-asserted-by":"publisher","first-page":"e2248685","DOI":"10.1001\/jamanetworkopen.2022.48685","volume":"6","author":"C Lee","year":"2023","unstructured":"Lee, C. et al. Development of a machine learning model for sonographic assessment of gestational age. JAMA Netw. Open 6, e2248685\u2013e2248685 (2023).","journal-title":"JAMA Netw. Open"},{"key":"1406_CR15","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1002\/pd.6059","volume":"42","author":"J Matthew","year":"2022","unstructured":"Matthew, J. et al. Exploring a new paradigm for the fetal anomaly ultrasound scan: artificial intelligence in real time. Prenat. Diagn. 42, 49\u201359 (2022).","journal-title":"Prenat. Diagn."},{"key":"1406_CR16","doi-asserted-by":"publisher","first-page":"102262","DOI":"10.1016\/j.media.2021.102262","volume":"75","author":"PJ Blanco","year":"2022","unstructured":"Blanco, P. J. et al. Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets. Med. Image Anal. 75, 102262 (2022).","journal-title":"Med. Image Anal."},{"key":"1406_CR17","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1067\/S0002-9378(03)00894-9","volume":"189","author":"MR Chavez","year":"2003","unstructured":"Chavez, M. R. et al. Fetal transcerebellar diameter nomogram in singleton gestations with special emphasis in the third trimester: a comparison with previously published nomograms. Am. J. Obstet. Gynecol. 189, 1021\u20131025 (2003).","journal-title":"Am. J. Obstet. Gynecol."},{"key":"1406_CR18","doi-asserted-by":"publisher","first-page":"e0200412","DOI":"10.1371\/journal.pone.0200412","volume":"13","author":"TLA van den Heuvel","year":"2018","unstructured":"van den Heuvel, T. L. A., de Bruijn, D., de Korte, C. L. & van Ginneken, B. Automated measurement of fetal head circumference using 2D ultrasound images. PloS ONE 13, e0200412 (2018).","journal-title":"PloS ONE"},{"key":"1406_CR19","first-page":"629","volume":"2","author":"R Smith","year":"2007","unstructured":"Smith, R. An overview of the tesseract ocr engine. Proc. Int. Conf. Doc. Anal. Recognit. ICDAR 2, 629\u2013633 (2007).","journal-title":"Proc. Int. Conf. Doc. Anal. Recognit. ICDAR"},{"key":"1406_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9351 (2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1406_CR21","unstructured":"BMUS 3rd Trimester Special Interest Group. Professional guidance for fetal growth scans performed after 23 weeks of gestation. https:\/\/www.bmus.org\/static\/uploads\/resources\/SIG3_document_FINAL__v_16__27_Jan_2022-_With_cover_QcOJnLN.pdf (2022)."},{"key":"1406_CR22","unstructured":"Voluson E8\/E8 Expert Basic User Manual (GE Healthcare, 2012)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01406-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01406-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01406-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T16:04:21Z","timestamp":1736611461000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01406-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,11]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1406"],"URL":"https:\/\/doi.org\/10.1038\/s41746-024-01406-z","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,11]]},"assertion":[{"value":"18 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare the following competing interests: All authors are co-inventors on a patent filing related to the core methods described in this work filed by King\u2019s College London (UK patent application number P333GB, pending). This patent covers the method used to obtain a single best estimate of biometrics, with credible intervals, described in this paper. All authors are co-founders and hold equity ownership in Fraiya Ltd., an entity that is commercialising this technology.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22"}}