{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T21:35:51Z","timestamp":1789421751847,"version":"build-2803163510"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first\u2010trimester screening for pre\u2010eclampsia in a large Asian population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This was a secondary analysis of a multicenter prospective cohort study in 10\u2009935 participants with singleton pregnancies attending for routine pregnancy care at 11\u201313\n                      <jats:sup>+6<\/jats:sup>\n                      \u2009weeks of gestation in seven regions in Asia between December 2016 and June 2018. We applied the AI+ML model for the first\u2010trimester prediction of preterm pre\u2010eclampsia (&lt;37\u2009weeks), term pre\u2010eclampsia (\u226537\u2009weeks), and any pre\u2010eclampsia, which was derived and tested in a cohort of pregnant participants in the UK (Model 1). This model comprises maternal factors with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor (PlGF). The model was further retrained with adjustments for analyzers used for biochemical testing (Model 2). Discrimination was assessed by area under the receiver operating characteristic curve (AUC). The Delong test was used to compare the AUC of Model 1, Model 2, and the Fetal Medicine Foundation (FMF) competing risk model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The predictive performance of Model 1 was significantly lower than that of the FMF competing risk model in the prediction of preterm pre\u2010eclampsia (0.82, 95% confidence interval [CI] 0.77\u20130.87 vs. 0.86, 95% CI 0.811\u20130.91,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.019), term pre\u2010eclampsia (0.75, 95% CI 0.71\u20130.80 vs. 0.79, 95% CI 0.75\u20130.83,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.006), and any pre\u2010eclampsia (0.78, 95% CI 0.74\u20130.81 vs. 0.82, 95% CI 0.79\u20130.84,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). Following the retraining of the data with adjustments for the PlGF analyzers, the performance of Model 2 for predicting preterm pre\u2010eclampsia, term pre\u2010eclampsia, and any pre\u2010eclampsia was improved with the AUC values increased to 0.84 (95% CI 0.80\u20130.89), 0.77 (95% CI 0.73\u20130.81), and 0.80 (95% CI 0.76\u20130.83), respectively. There were no differences in AUCs between Model 2 and the FMF competing risk model in the prediction of preterm pre\u2010eclampsia (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.135) and term pre\u2010eclampsia (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.084). However, Model 2 was inferior to the FMF competing risk model in predicting any pre\u2010eclampsia (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.024).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study has demonstrated that following adjustment for the biochemical marker analyzers, the predictive performance of the AI+ML prediction model for pre\u2010eclampsia in the first trimester was comparable to that of the FMF competing risk model in an Asian population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ijgo.15563","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T03:53:26Z","timestamp":1714103606000},"page":"350-359","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Validation of the first\u2010trimester machine learning model for predicting pre\u2010eclampsia in an Asian population"],"prefix":"10.1002","volume":"167","author":[{"given":"Long","family":"Nguyen\u2010Hoang","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, Prince of Wales Hospital The Chinese University of Hong Kong  Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daljit S.","family":"Sahota","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Prince of Wales Hospital The Chinese University of Hong Kong  Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ritsuko K.","family":"Pooh","sequence":"additional","affiliation":[{"name":"CRIFM Prenatal Medical Clinic  Osaka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honglei","family":"Duan","sequence":"additional","affiliation":[{"name":"Nanjing Drum Tower Hospital  Nanjing 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