{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:58:47Z","timestamp":1781690327318,"version":"3.54.5"},"reference-count":17,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"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; Obste"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To assess whether machine learning (ML) offers improved birth weight prediction accuracy, since despite numerous models, the Hadlock formula remains the clinical standard.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A multicenter retrospective study analyzed data from 9674 singleton pregnancies with estimated fetal weight (EFW) within 7\u2009days of delivery. ML models\u2014Linear Regression, Decision Tree, Random Forest, LightGBM, XGBoost, and Neural Networks\u2014were trained using ultrasound and maternal features. Performance was measured by mean absolute percentage error (MAPE), root mean squared error (RMSE), mean absolute error (MAE), accuracy, precision, recall, and F1\u2010score for percentile categories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LightGBM and XGBoost outperformed Hadlock in overall weight estimation (MAPE ~0.065; RMSE ~252; MAE ~190). For birth weight percentiles (&lt;3rd, &lt;10th, &gt;90th, &gt;97th), ML showed marginal or comparable improvement. LightGBM had higher accuracy and F1 for extreme percentiles, whereas Hadlock showed slightly better recall in some cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML models, especially LightGBM and XGBoost, enhanced overall weight prediction but offered limited gains in identifying percentile\u2010based risk. The Hadlock formula remains a strong tool for categorizing at\u2010risk fetuses.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ijgo.70657","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T11:53:26Z","timestamp":1762862006000},"page":"456-462","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning versus traditional formulas for fetal weight estimation: An international multicenter study evaluating prediction accuracy across birth weight percentiles"],"prefix":"10.1002","volume":"173","author":[{"given":"Omer","family":"Dor","sequence":"first","affiliation":[{"name":"Gray Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eran","family":"Ashwal","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology McMaster University  Hamilton Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"May","family":"Cohen","sequence":"additional","affiliation":[{"name":"Gray Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ori","family":"Rottenstreich","sequence":"additional","affiliation":[{"name":"The Henry and Marilyn Taub Faculty of Computer Science Technion Israel Institute of Technology  Haifa Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yariv","family":"Yogev","sequence":"additional","affiliation":[{"name":"Gray Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"},{"name":"Tel Aviv Sourasky Medical Center  Tel Aviv\u2010Yafo Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9913-6124","authenticated-orcid":false,"given":"Noam","family":"Shomron","sequence":"additional","affiliation":[{"name":"Gray Faculty of Medical and Health Sciences Tel Aviv University  Tel Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Misgav","family":"Rottenstreich","sequence":"additional","affiliation":[{"name":"School of Medicine Hebrew University of Jerusalem  Jerusalem Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.20272"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0000000000001767"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0000000000003070"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9378(85)90298-4"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.19066"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1023\/a:1016409317640"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1023\/a:1010933404324"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_10_10_1","unstructured":"LightGBM | Proceedings of the 31st International Conference on Neural Information Processing Systems. 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