{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:56:53Z","timestamp":1790575013705,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T00:00:00Z","timestamp":1788048000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T00:00:00Z","timestamp":1788048000000},"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":["Ultrasound in Obstet &amp;amp; Gyne"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the performance of a machine\u2010learning (ML) model compared with traditional logistic regression models for predicting a large\u2010for\u2010gestational\u2010age (LGA) neonate at term and associated adverse perinatal outcomes, using sonographic biometric and Doppler parameters routinely assessed at a late third\u2010trimester scan in combination with maternal demographic characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This cohort study involved a retrospective data analysis of singleton pregnancies that underwent a routine third\u2010trimester ultrasound examination at 35\u2009+\u20090 to 37\u2009+\u20096\u2009weeks' gestation and were delivered\u00a0in a single tertiary referral center between December 2019 and December 2025. Pregnancies with major fetal structural anomalies, known genetic abnormalities or missing outcome data were excluded. We collected maternal demographic and obstetric characteristics, first\u2010trimester biochemical screening results, third\u2010trimester fetal biometry and Doppler parameters, and birth and neonatal outcome data. Both traditional statistical and contemporary ML models were developed to predict a LGA neonate (birth weight\u2009&gt;\u200990\n                      <jats:sup>th<\/jats:sup>\n                      \u2009centile), as well as LGA complicated by one or more predefined adverse perinatal outcomes. Pregnancies with LGA complicated by adverse maternal outcome and those with adverse neonatal outcome were also analyzed separately to evaluate outcome\u2010specific predictors and improve clinical interpretability. Logistic regression was used to identify predictors of each outcome with model discrimination assessed by the area under the receiver\u2010operating\u2010characteristics curve (AUC). In parallel, ML models (random forest, eXtreme Gradient Boosting (XGBoost), elastic net and support vector machine) were applied to capture potential non\u2010linear relationships and interactions, providing complementary modeling approaches across different data structures.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study included 21\u2009743 singleton pregnancies, with a median gestational age of 36\u2009+\u20093 (interquartile range (IQR), 36\u2009+\u20092 to 36\u2009+\u20095)\u00a0weeks at third\u2010trimester ultrasound examination and 39\u2009+\u20094 (IQR, 39\u2009+\u20090 to 40\u2009+\u20094)\u00a0weeks at delivery, and a median birth\u2010weight centile of 58.8 (IQR, 32.2\u201380.9). There were 3007 (13.8%) LGA neonates and 689 (3.2%) cases of LGA neonate with adverse outcome. The best\u2010performing logistic regression and ML models demonstrated high discriminatory performance for predicting a LGA neonate (AUC, 0.888 (95%\u2009CI, 0.874\u20130.901) for both logistic regression and ML models) as well as LGA neonate with adverse perinatal outcome (AUC, 0.859 (95%\u2009CI, 0.831\u20130.884) for logistic regression and AUC, 0.859 (95%\u2009CI, 0.831\u20130.883) for ML model). Across both traditional logistic regression and ML models, the estimated fetal weight (EFW) centile was the most robust single predictor across all outcomes, with AUCs that ranged from 0.809 to 0.878. Model\u2010specific 95%\u2009CIs are provided. Notably, ML models did not improve predictive performance compared to logistic regression. SHapley Additive exPlanations (SHAP) analysis revealed that EFW and abdominal circumference centiles were the principal determinants of risk prediction in our cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Routinely collected third\u2010trimester ultrasound data can identify pregnancies at risk of LGA and associated adverse outcomes in an unselected population. EFW was the dominant predictor in both traditional logistic regression and ML models, with minimal incremental value obtained from additional maternal or ultrasound variables. ML did not improve predictive performance over traditional logistic regression, indicating that fetal size alone captured most clinically relevant information for risk stratification. These findings support a simpler, more clinically actionable approach to risk stratification, shifting focus from identifying large fetuses to predicting clinically meaningful complications. \u00a9 2026 International Society of Ultrasound in Obstetrics and Gynecology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/uog.70317","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:23:39Z","timestamp":1788150219000},"page":"361-371","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning for prediction of large\u2010for\u2010gestational\u2010age neonate with adverse outcome at routine ultrasound examination at 35\u201337\u2009weeks"],"prefix":"10.1002","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5304-2296","authenticated-orcid":false,"given":"M.","family":"Lopian","sequence":"first","affiliation":[{"name":"Fetal Medicine, St George's University Hospitals NHS Foundation Trust University of London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7931-5172","authenticated-orcid":false,"given":"C. O.","family":"Ulusoy","sequence":"additional","affiliation":[{"name":"Perinatology Department Ministry of Health, Bing\u00f6l State Hospital  Bing\u00f6l Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1724-811X","authenticated-orcid":false,"given":"D.","family":"Mohammed","sequence":"additional","affiliation":[{"name":"Fetal Medicine, St George's University Hospitals NHS Foundation Trust University of London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Segal","sequence":"additional","affiliation":[{"name":"Tel Aviv University Faculty of Medicine  Tel Aviv Israel"},{"name":"Sheba Medical Center, Tel Hashomer  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2802-7670","authenticated-orcid":false,"given":"A.","family":"Khalil","sequence":"additional","affiliation":[{"name":"Fetal Medicine, St George's University Hospitals NHS Foundation Trust University of London  London UK"},{"name":"Vascular Biology Research Centre Molecular and Clinical Sciences Research Institute, City St George's University of London  London UK"},{"name":"Fetal Medicine Unit, Liverpool Women's Hospital  Liverpool UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,30]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.26305"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.27485"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejogrb.2024.07.021"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00404-008-0630-7"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.29267"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.70117"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/1471-0528.17802"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.27538"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/14767058.2018.1511697"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.AOG.0000140682.63746.be"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1056064"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.20132"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(22)01191-6"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/oby.21532"},{"key":"e_1_2_6_16_1","article-title":"Gestational diabetes mellitus and adverse maternal and perinatal outcomes in twin and singleton pregnancies: a systematic review and meta\u2010analysis","volume":"200","author":"Zhang Y","year":"2023","journal-title":"Diabetes Res Clin Pract"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12909-023-04698-z"},{"key":"e_1_2_6_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-018-1971-2"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9378(85)90298-4"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.17347"},{"key":"e_1_2_6_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(14)60932-6"},{"key":"e_1_2_6_22_1","volume-title":"IBM SPSS Statistics for Windows, Version 27.0","author":"IBM Corp","year":"2020"},{"key":"e_1_2_6_23_1","unstructured":"Python Software Foundation.Python Language Reference Version 3.12.2.2023Available athttps:\/\/www.python.org"},{"key":"e_1_2_6_24_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_6_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_6_26_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00503.x"},{"key":"e_1_2_6_27_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011215321374"},{"key":"e_1_2_6_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2024.04.052"},{"key":"e_1_2_6_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.15780"},{"key":"e_1_2_6_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.20377"},{"key":"e_1_2_6_31_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1003190"},{"key":"e_1_2_6_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.29223"},{"key":"e_1_2_6_33_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2024-091258"},{"key":"e_1_2_6_34_1","doi-asserted-by":"publisher","DOI":"10.2337\/dc18-0551"}],"container-title":["Ultrasound in Obstetrics &amp; 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