{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T08:43:57Z","timestamp":1790671437991,"version":"4.1.0"},"reference-count":18,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["MOST 112WFA0110012"],"award-info":[{"award-number":["MOST 112WFA0110012"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The current study evaluates the efficacy of artificial intelligence (AI)\u2013assisted measurement of cervical length (CL) in predicting spontaneous preterm birth (sPTB), comparing the traditional single\u2010line and two\u2010line methods with the innovative AI\u2010line method in the first trimester of pregnancy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>This study is a retrospective secondary analysis of ultrasound images collected prospectively from women with a viable singleton pregnancy who were undergoing Down syndrome screening at Prince of Wales Hospital, Hong Kong SAR. CL was measured using transvaginal ultrasound, with a secondary analysis of archived 1664 images acquired during a prospective study and processed through a ResUNet\u2010based model. This model, combining UNet and ResNet architectures, a modified ResUNet framework, aimed to overcome the limitations of current measurement techniques by providing a more accurate prediction of CL, particularly in cases where the cervix is curved.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The AI\u2010line method demonstrated superior accuracy in predicting sPTB at &lt;37 and &lt;32\u2009weeks of gestation compared with conventional methods, with higher areas under the receiver operating characteristic curve (AUROC). The AUROC of CL measured by the AI\u2010line method (0.676 [95% CI, 0.616\u20130.735],\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05) in predicting sPTB at &lt;37\u2009weeks of gestation was significantly higher than the single\u2010line (0.537 [95% CI, 0.474\u20130.6]) and two\u2010line (0.54 [95% CI, 0.473\u20130.66]) methods. For the prediction of sPTB at &lt;32\u2009weeks of gestation, the AI\u2010line method achieved an AUROC of 0.777 (95% CI, 0.703\u20130.850).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The AI\u2010line method offers a more accurate measurement of CL in the first trimester, showing potential as a tool for early screening of sPTB risk. The study's results could significantly influence clinical decision\u2010making, providing a basis for the potential future clinical application of AI in prenatal care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ijgo.70744","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T09:29:36Z","timestamp":1767950976000},"page":"1421-1432","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence application in the prediction of spontaneous preterm birth by cervical length in the first trimester of pregnancy: Comparison of three measurement methods"],"prefix":"10.1002","volume":"173","author":[{"given":"Yi\u2010yun","family":"Tai","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology National Taiwan University Hospital  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bor\u2010Yann","family":"Tseng","sequence":"additional","affiliation":[{"name":"Department of Engineering Science National Cheng Kung University  Tainan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu\u2010Han","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Engineering Science National Cheng Kung University  Tainan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi\u2010Hua","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Engineering Science National Cheng Kung University  Tainan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3944-4130","authenticated-orcid":false,"given":"Liona C.","family":"Poon","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynaecology The Chinese University of Hong Kong  Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3109\/14767058.2010.501124"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.12465"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejogrb.2013.07.042"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/14767058.2018.1425990"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.7863\/jum.2011.30.7.997"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/aogs.14138"},{"key":"e_1_2_10_8_1","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: Convolutional networks for biomedical image segmentation. 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