{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:33:10Z","timestamp":1791819190534,"version":"4.3.4"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Fetal growth restriction (FGR) is a significant concern for clinicians and pregnant women, as it is associated with increased fetal and neonatal mortality and morbidity. Although ultrasound has been the gold standard for many years to define FGR, it remains less than ideal for early detection of FGR. Placental dysfunction is a key factor in the development of FGR. The objective of this study is to achieve the early detection of FGR through the utilization of placental ultrasound images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A retrospective analysis was conducted using 80 placental ultrasound images from 40 FGR fetuses and 40 normal fetuses matched for gestational age. Approximately 300 texture features were extracted from the placental images using key texture feature selection and histogram of oriented gradients (HOG) extraction methods. These features were then re-encoded using a bag-of-visual-words model with weight scaling, resulting in more effective features. The encoded image features were used to train a classifier, and ensemble prediction techniques were used to improve classification accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Result<\/jats:title><jats:p>In this study, we applied the proposed method alongside several popular image classification methods for predicting FGR. The proposed method achieved the best experimental results, with an accuracy of 70% and an F1 score of 0.7653. We also compared different feature extraction methods separately, and the experimental results showed that HOG feature extraction is more suitable for feature extraction of ultrasound placental images. Finally, we plotted the receiver operating characteristic (ROC) curve with an area under the curve (AUC) value of 0.80.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>To enable early prediction of FGR, we propose a visual bag-of-words model based on weight scaling for analyzing placental ultrasound images in the early stages\u2014before significant fetal impairment occurs. The proposed model shows strong potential to assist doctors in making preliminary assessments, thereby facilitating earlier intervention. This can help reduce the risk of harm to both fetuses and pregnant women.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1529666","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T05:28:04Z","timestamp":1750138084000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A weighted bag of visual words model for predicting fetal growth restriction at an early stage"],"prefix":"10.3389","volume":"12","author":[{"given":"Ani","family":"Dong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiling","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s13224-019-01278-4","article-title":"Fetal growth restriction","volume":"70","author":"Shrivastava","year":"2020","journal-title":"J Obstet Gynecol India."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1519048","DOI":"10.1155\/2019\/1519048","article-title":"Fetal growth restriction prediction: how to move beyond","volume":"2019","author":"Leite","year":"2019","journal-title":"Sci World J."},{"key":"B3","article-title":"Fetal growth restriction","volume-title":"StatPearls","author":"Chew","year":"2024"},{"key":"B4","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.placenta.2019.09.003","article-title":"Maternal vascular malformation in the placenta is an indicator for fetal growth restriction irrespective of neonatal birthweight","volume":"87","author":"Hendrix","year":"2019","journal-title":"Placenta."},{"key":"B5","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1002\/uog.20272","article-title":"ISUOG Practice Guidelines: ultrasound assessment of fetal biometry and growth","volume":"53","author":"Salomon","year":"2019","journal-title":"Ultrasound Obstet Gynecol."},{"key":"B6","first-page":"253","article-title":"Texture-based analysis of fetal organs in fetal growth restriction","volume-title":"Proceedings of the 3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE 2021); 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