{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:53:42Z","timestamp":1785423222711,"version":"3.56.0"},"reference-count":19,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T00:00:00Z","timestamp":1750464000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001411","name":"Indian Council of Medical Research","doi-asserted-by":"publisher","award":["5\/7\/71\/MH\/Adhoc\/2020"],"award-info":[{"award-number":["5\/7\/71\/MH\/Adhoc\/2020"]}],"id":[{"id":"10.13039\/501100001411","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":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This study considers the application of ultrasound placental image texture analysis for the prediction of hypertensive disorders of pregnancy (HDP) using deep learning (DL) algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>In this prospective observational study, placental ultrasound images were taken serially at 11\u201314\u2009weeks (T1), 20\u201324\u2009weeks (T2), and 28\u201332\u2009weeks (T3). Pregnant women with blood pressure at or above 140\/90\u2009mmHg on two occasions 4\u2009h apart were considered to have HDP. The image data of women with HDP were compared with those with a normal outcome using DL techniques such as convolutional neural networks (CNN), transfer learning, and a Vision Transformer (ViT) with a TabNet classifier. The accuracy and the Cohen kappa scores of the different DL techniques were compared.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 600\/1008 (59.5%) subjects had a normal outcome, and 143\/1008 (14.2%) had HDP; the reminder, 265\/1008 (26.3%), had other adverse outcomes. In the basic CNN model, the accuracy was 81.6% for T1, 80% for T2, and 82.8% for T3. Using the Efficient Net B0 transfer learning model, the accuracy was 87.7%, 85.3%, and 90.3% for T1, T2, and T3, respectively. Using a TabNet classifier with a ViT, the accuracy and area under the receiver operating characteristic curve scores were 91.4% and 0.915 for T1, 90.2% and 0.904 for T2, and 90.3% and 0.907 for T3. The sensitivity and specificity for HDP prediction using ViT were 89.1% and 91.7% for T1, 86.6% and 93.7% for T2, and 85.6% and 94.6% for T3.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Ultrasound placental image texture analysis using DL could differentiate women with a normal outcome and those with HDP with excellent accuracy and could open new avenues for research in this field.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ijgo.70306","type":"journal-article","created":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T04:08:36Z","timestamp":1750478916000},"page":"1294-1301","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ultrasound placental image texture analysis using artificial intelligence and deep learning models to predict hypertension in pregnancy"],"prefix":"10.1002","volume":"171","author":[{"given":"Urvashi","family":"Arora","sequence":"first","affiliation":[{"name":"Lady Hardinge Medical College IIIT Delhi  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Palani","family":"Vigneshwar","sequence":"additional","affiliation":[{"name":"Lady Hardinge Medical College IIIT Delhi  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Munagala Krishna","family":"Sai","sequence":"additional","affiliation":[{"name":"Lady Hardinge Medical College IIIT Delhi  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reena","family":"Yadav","sequence":"additional","affiliation":[{"name":"Lady Hardinge Medical College, Department of Obstetrics and Gynecology LHMC  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debarka","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Lady Hardinge Medical College IIIT Delhi  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7472-2020","authenticated-orcid":false,"given":"Manisha","family":"Kumar","sequence":"additional","affiliation":[{"name":"Lady Hardinge Medical College, Department of Obstetrics and Gynecology LHMC  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,6,21]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.placenta.2014.06.375"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1159\/000324318"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.12802"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.12301"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2023.1126780"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph182212191"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2018.11.020"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-29105-x"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00404-010-1555-5"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27036"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging8030081"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.preghy.2021.09.008"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radi.2022.09.008"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/14767058.2021.1887847"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-61055-6"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14030716"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102639"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.preghy.2022.10.003"}],"container-title":["International Journal of Gynecology &amp; 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