{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:54:02Z","timestamp":1786978442352,"version":"build-2736575974"},"reference-count":32,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T00:00:00Z","timestamp":1736985600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Prenatal Diagnosis"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The first objective is to develop a nuchal thickness reference chart. The second objective is to compare rule\u2010based algorithms and machine learning models in predicting small\u2010for\u2010gestational\u2010age infants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>This retrospective study involved singleton pregnancies at University Malaya Medical Centre, Malaysia, developed a nuchal thickness chart and evaluated its predictive value for small\u2010for\u2010gestational\u2010age using Malaysian and Singapore cohorts. Predictive performance using conjunctive (AND)\/disjunctive (OR) rule\u2010based algorithms was assessed. Seven machine learning models were trained on Malaysia data and evaluated on both Malaysia and Singapore cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      5519 samples were collected from the University Malaya Medical Centre. Small\u2010for\u2010gestational\u2010age infants exhibit significantly lower nuchal thickness (small\u2010for\u2010gestational\u2010age: 4.57 [1.04] mm, appropriate\u2010for\u2010gestational\u2010age: 4.86 [1.06] mm,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001). Implementing disjunctive rule (nuchal thickness\u00a0&lt;\u00a010th centile or estimated fetal weight\u00a0&lt;\u00a010th centile) significantly improved small\u2010for\u2010gestational\u2010age prediction across all growth charts, with balanced accuracy gains of 5.83% in Malaysia (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05) and 7.75% in Singapore. The best model for predicting small\u2010for\u2010gestational\u2010age was: logistic regression with five variables (abdominal circumference, femur length, nuchal thickness, maternal age, and ultrasound\u2010confirmed gestational age), which achieved an area under the curve of 0.75 for Malaysia cohorts; support vector machine with all variables, achieved area under the curve of 0.81 for Singapore cohorts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Small\u2010for\u2010gestational\u2010age infants demonstrate significantly reduced second\u2010trimester nuchal thickness. Employing the disjunctive rule enhanced small\u2010for\u2010gestational\u2010age prediction. Logistic regression and support vector machines show superior performance among all models, highlighting the advantages of machine learning. Larger prospective studies are needed to assess clinical utility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pd.6748","type":"journal-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T08:50:51Z","timestamp":1737017451000},"page":"374-386","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing Small\u2010for\u2010Gestational\u2010Age Prediction: Multi\u2010Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4756-7188","authenticated-orcid":false,"given":"Jiaxuan","family":"Deng","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neha Sethi A\/P.","family":"Naresh Sethi","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynaecology Faculty of Medicine Universiti Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azanna","family":"Ahmad Kamar","sequence":"additional","affiliation":[{"name":"Department of Paediatrics Faculty of Medicine Universiti Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahmah","family":"Saaid","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynaecology Faculty of Medicine Universiti Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chu Kiong","family":"Loo","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0068-6957","authenticated-orcid":false,"given":"Citra Nurfarah Zaini","family":"Mattar","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynaecology National University Hospital  Singapore Singapore"},{"name":"Department of Obstetrics and Gynaecology Yong Loo Lin School of Medicine National University of Singapore  Singapore Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nurul Syazwani","family":"Jalil","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynaecology Faculty of Medicine Universiti Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shier Nee","family":"Saw","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,1,16]]},"reference":[{"key":"e_1_2_12_2_1","unstructured":"World Health Organization.Joint Low Birthweight Estimates accessed February 25 2024 https:\/\/www.who.int\/teams\/nutrition\u2010and\u2010food\u2010safety\/monitoring\u2010nutritional\u2010status\u2010and\u2010food\u2010safety\u2010and\u2010events\/joint\u2010low\u2010birthweight\u2010estimates#:~:text=The%20UNICEF%2DWHO%20Low%20Birthweight birthweight%20(compared%20to%202012)."},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.23638"},{"key":"e_1_2_12_4_1","first-page":"36","volume-title":"Neonatal Hypoglycemia,\u201d Seminars in Fetal and Neonatal Medicine","author":"Adamkin D. 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