{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T21:35:57Z","timestamp":1789421757741,"version":"build-2803163510"},"reference-count":24,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Ultrasound Medicine"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>Birthweight prediction in fetal development presents a challenge in direct measurement and often depends on empirical formulas based on the clinician's experience. Existing methods suffer from low accuracy and high execution times, limiting their clinical effectiveness. This study aims to introduce a novel approach integrating feature\u2010wise linear modulation (FiLM), gated recurrent unit (GRU), and Attention network to improve birthweight prediction using ultrasound data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The proposed method utilizes FiLM for adaptive modulation, dynamically adjusting layer activations based on input specifics for enhanced information extraction. GRU is employed to capture sequential dependencies, recognizing the evolving maternal and fetal parameters during pregnancy. The Attention network selectively focuses on crucial parameters, dynamically adjusting feature weights for accurate predictions. The study evaluates classification accuracies for three groups: appropriate\u2010for\u2010gestational\u2010age, large\u2010for\u2010gestational\u2010age, and small\u2010for\u2010gestational\u2010age (SGA). Prediction errors are minimized by optimizing parameters and using mean squared error as the loss function. Experimental evaluations are performed using multiple metrics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed strategy attains a high prediction accuracy of 98.8%, outperforming existing methods such as ensemble transfer learning model (83.5%), BabyNet++ (91.7%), bi\u2010directional LSTM with CNN and a hybrid whale with oppositional fruit fly optimization (89.2%), linear regression\u2013random forest\u2013artificial neural network (79.5%), and Attention MFP\u2010Unet (93.6%). The integrated network provides advanced insights into birthweight dynamics, enhancing both interpretability and accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The findings of this study are vital for birthweight prediction, clinical delivery guideline development, and implementation of decision\u2010making. The proposed approach supports clinicians in making informed decisions during obstetric examinations and assists pregnant women in weight management, showcasing significant advancements in maternal healthcare.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jum.16633","type":"journal-article","created":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T12:03:58Z","timestamp":1735214638000},"page":"711-725","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improved Birthweight Prediction With Feature\u2010Wise Linear Modulation, <scp>GRU<\/scp>, and Attention Mechanism in Ultrasound Data"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3889-4847","authenticated-orcid":false,"given":"G.","family":"Mohana Priya","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering SRM Institute of Science and Technology, Vadapalani Campus  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. 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B.","family":"Sangeetha","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering SRM Institute of Science and Technology, Vadapalani Campus  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,12,26]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajogmf.2023.101123"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105338"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/ajum.12239"},{"key":"e_1_2_8_5_1","first-page":"136","article-title":"An approach for fetal weight estimation using machine learning for women safety","author":"Bijapur S","year":"2023","journal-title":"J Sci Res Technol"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s44294-024-00033-z"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.26305"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/make6020036"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/CM9.0000000000001413"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-024-19566-8"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-023-05819-8"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0267190"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01981-9"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-14393-6"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfma.2021.09.018"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6665573"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-023-06128-w"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-021-03660-5"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/s24010002"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-021-01388-y"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9995"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2021.06.020"},{"key":"e_1_2_8_23_1","first-page":"350","volume-title":"International Conference on Medical Image Computing and Computer\u2010Assisted Intervention","author":"P\u0142otka S","year":"2022"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107602"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12092229"}],"container-title":["Journal of Ultrasound in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jum.16633","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T04:09:04Z","timestamp":1747886944000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jum.16633"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,26]]},"references-count":24,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["10.1002\/jum.16633"],"URL":"https:\/\/doi.org\/10.1002\/jum.16633","archive":["Portico"],"relation":{},"ISSN":["0278-4297","1550-9613"],"issn-type":[{"value":"0278-4297","type":"print"},{"value":"1550-9613","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,26]]},"assertion":[{"value":"2024-05-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}