{"id":23729,"date":"2025-05-15T13:40:55","date_gmt":"2025-05-15T11:40:55","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&p=23729"},"modified":"2025-05-15T13:48:51","modified_gmt":"2025-05-15T11:48:51","slug":"direct-estimation-of-fetal-biometry-measurements-from-ultrasound-video-scans-through-deep-learning-2","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/direct-estimation-of-fetal-biometry-measurements-from-ultrasound-video-scans-through-deep-learning-2\/","title":{"rendered":"Direct estimation of fetal biometry measurements from ultrasound video scans through deep learning"},"content":{"rendered":"\n

Szymon P\u0142otka, Karol Pustelnik, Paula Szenejko, Kinga \u017bebrowska, Iga Rzucid\u0142o-Szyma\u0144ska, Natalia Szymecka-Samaha, Tomasz \u0141\u0119gowik, Katarzyna Kosi\u0144ska-Kaczy\u0144ska, Przemys\u0142aw Korzeniowski, Piotr Bili\u0144ski, Asma Khalil, Robert Brawura-Biskupski-Samaha, Ivana I\u0161gum, Clara I S\u00e1nchez, Arkadiusz Sitek\u00a0<\/h2>\n\n\n\n
<\/div>\n\n\n\n

In this collaborative study, the authors propose a deep learning-based method aimed at improving the precision and efficiency of fetal biometry in prenatal ultrasound.<\/p>\n\n\n\n

The study, titled “Direct estimation of fetal biometry measurements from ultrasound video scans through deep learning,” tackles a long-standing challenge in obstetric imaging: the reliance on skilled professionals to manually identify specific standard anatomical planes\u2014such as those of the fetal head, abdomen, and femur\u2014for measurement. This traditional approach demands expertise, is time-intensive, and often results in inconsistencies due to intra- and interobserver variation.<\/p>\n\n\n\n

To address these limitations, the authors developed an end-to-end deep learning framework capable of automatically identifying the required standard planes and directly extracting key biometric measurements from entire ultrasound video sequences. Unlike previous methods that require manual frame selection or partial automation, this approach performs both detection and measurement in a unified pipeline.<\/p>\n\n\n\n

Importantly, it is\u2014according to the authors\u2014the first method to estimate the full set of fetal biometry parameters directly from ultrasound video data without human intervention. This advancement has the potential to improve diagnostic consistency and reduce operator dependency, particularly in resource-constrained settings, marking a significant step forward for AI-assisted prenatal diagnostics.<\/p>\n\n\n\n

<\/div>\n\n\n\n\t\n \n \n\t\t\t\n\n\t\t\t\t\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n \n \n\n\n\n
<\/div>\n\n\n\n

Autors<\/strong>: Szymon P\u0142otka<\/a>, Karol Pustelnik, Paula Szenejko, Kinga \u017bebrowska, Iga Rzucid\u0142o-Szyma\u0144ska, Natalia Szymecka-Samaha, Tomasz \u0141\u0119gowik, Katarzyna Kosi\u0144ska-Kaczy\u0144ska, Przemys\u0142aw Korzeniowski<\/a>, Piotr Bili\u0144ski, Asma Khalil, Robert Brawura-Biskupski-Samaha, Ivana I\u0161gum, Clara I S\u00e1nchez, Arkadiusz Sitek\u00a0<\/p>\n\n\n\n

DOI<\/strong>: 10.1016\/j.ajogmf.2025.101623 <\/p>\n\n\n\n

Keywords:<\/strong> deep learning in medicine, fetal biometry, ultrasound video analysis, prenatal ultrasound<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Article in journal: American Journal of Obstetrics & Gynecology MFM, 2025<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[17],"class_list":["post-23729","research","type-research","status-publish","hentry","research_type-publications","research_team-health-informatics-group-higs"],"yoast_head":"\nDirect estimation of fetal biometry measurements from ultrasound video scans through deep learning - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/direct-estimation-of-fetal-biometry-measurements-from-ultrasound-video-scans-through-deep-learning-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Direct estimation of fetal biometry measurements from 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Rzucid\u0142o-Szyma\u0144ska, Natalia Szymecka-Samaha, Tomasz \u0141\u0119gowik, Katarzyna Kosi\u0144ska-Kaczy\u0144ska, Przemys\u0142aw Korzeniowski, Piotr Bili\u0144ski, Asma Khalil, Robert Brawura-Biskupski-Samaha, Ivana I\u0161gum, Clara I S\u00e1nchez, Arkadiusz Sitek\u00a0<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-szymon-plotka-karol-pustelnik-paula-szenejko-kinga-zebrowska-iga-rzucidlo-szymanska-natalia-szymecka-samaha-tomasz-legowik-katarzyna-kosinska-kaczynska-przemyslaw-korzeniowski-piotr-bilinski-asma-khalil-robert-brawura-biskupski-samaha-ivana-isgum-clara-i-sanchez-arkadiusz-sitek\">Szymon P\u0142otka, Karol Pustelnik, Paula Szenejko, Kinga \u017bebrowska, Iga Rzucid\u0142o-Szyma\u0144ska, Natalia Szymecka-Samaha, Tomasz \u0141\u0119gowik, Katarzyna Kosi\u0144ska-Kaczy\u0144ska, Przemys\u0142aw Korzeniowski, Piotr Bili\u0144ski, Asma Khalil, Robert Brawura-Biskupski-Samaha, Ivana I\u0161gum, Clara I S\u00e1nchez, Arkadiusz Sitek\u00a0<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-NS0cQ3","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-YA57he","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">In this collaborative study, the authors propose a deep learning-based method aimed at improving the precision and efficiency of fetal biometry in prenatal ultrasound.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">In this collaborative study, the authors propose a deep learning-based method aimed at improving the precision and efficiency of fetal biometry in prenatal ultrasound.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-YA57he","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The study, titled \"Direct estimation of fetal biometry measurements from ultrasound video scans through deep learning,\" tackles a long-standing challenge in obstetric imaging: the reliance on skilled professionals to manually identify specific standard anatomical planes\u2014such as those of the fetal head, abdomen, and femur\u2014for measurement. This traditional approach demands expertise, is time-intensive, and often results in inconsistencies due to intra- and interobserver variation.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The study, titled \"Direct estimation of fetal biometry measurements from ultrasound video scans through deep learning,\" tackles a long-standing challenge in obstetric imaging: the reliance on skilled professionals to manually identify specific standard anatomical planes\u2014such as those of the fetal head, abdomen, and femur\u2014for measurement. This traditional approach demands expertise, is time-intensive, and often results in inconsistencies due to intra- and interobserver variation.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-KwIbo8","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">To address these limitations, the authors developed an end-to-end deep learning framework capable of automatically identifying the required standard planes and directly extracting key biometric measurements from entire ultrasound video sequences. Unlike previous methods that require manual frame selection or partial automation, this approach performs both detection and measurement in a unified pipeline.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">To address these limitations, the authors developed an end-to-end deep learning framework capable of automatically identifying the required standard planes and directly extracting key biometric measurements from entire ultrasound video sequences. Unlike previous methods that require manual frame selection or partial automation, this approach performs both detection and measurement in a unified pipeline.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-L0zWmC","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Importantly, it is\u2014according to the authors\u2014the first method to estimate the full set of fetal biometry parameters directly from ultrasound video data without human intervention. This advancement has the potential to improve diagnostic consistency and reduce operator dependency, particularly in resource-constrained settings, marking a significant step forward for AI-assisted prenatal diagnostics.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Importantly, it is\u2014according to the authors\u2014the first method to estimate the full set of fetal biometry parameters directly from ultrasound video data without human intervention. This advancement has the potential to improve diagnostic consistency and reduce operator dependency, particularly in resource-constrained settings, marking a significant step forward for AI-assisted prenatal diagnostics.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-NS0cQ3","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/www.ajogmfm.org\/article\/S2589-9333(25)00024-2\/abstract","button_style":"primary","target":"_self","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-NS0cQ3","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-j8TR4a","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: <a href=\"https:\/\/sano.science\/people\/szymon-plotka\/\">Szymon P\u0142otka<\/a>, Karol Pustelnik, Paula Szenejko, Kinga \u017bebrowska, Iga Rzucid\u0142o-Szyma\u0144ska, Natalia Szymecka-Samaha, Tomasz \u0141\u0119gowik, Katarzyna Kosi\u0144ska-Kaczy\u0144ska, <a href=\"https:\/\/sano.science\/people\/przemyslaw-korzeniowski\/\">Przemys\u0142aw Korzeniowski<\/a>, Piotr Bili\u0144ski, Asma Khalil, Robert Brawura-Biskupski-Samaha, Ivana I\u0161gum, Clara I S\u00e1nchez, Arkadiusz Sitek\u00a0<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: <a href=\"https:\/\/sano.science\/people\/szymon-plotka\/\">Szymon P\u0142otka<\/a>, Karol Pustelnik, Paula Szenejko, Kinga \u017bebrowska, Iga Rzucid\u0142o-Szyma\u0144ska, Natalia Szymecka-Samaha, Tomasz \u0141\u0119gowik, Katarzyna Kosi\u0144ska-Kaczy\u0144ska, <a href=\"https:\/\/sano.science\/people\/przemyslaw-korzeniowski\/\">Przemys\u0142aw Korzeniowski<\/a>, Piotr Bili\u0144ski, Asma Khalil, Robert Brawura-Biskupski-Samaha, Ivana I\u0161gum, Clara I S\u00e1nchez, Arkadiusz Sitek\u00a0<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-j8TR4a","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1016\/j.ajogmf.2025.101623 <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1016\/j.ajogmf.2025.101623 <\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-mJrtPh","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Keywords:<\/strong> deep learning in medicine, fetal biometry, ultrasound video analysis, prenatal ultrasound<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Keywords:<\/strong> deep learning in medicine, fetal biometry, ultrasound video analysis, prenatal ultrasound<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-AQ3sIC","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><\/p>\n"]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/23729","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":19,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/23729\/revisions"}],"predecessor-version":[{"id":23752,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/23729\/revisions\/23752"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=23729"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=23729"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=23729"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}