{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T09:10:43Z","timestamp":1767345043715,"version":"3.48.0"},"reference-count":32,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Ultrasound Medicine"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the performance of artificial intelligence (AI)\u2010based models in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This diagnostic accuracy study analyzed ultrasound images of fetal livers from pregnancies between 37 and 42\u2009weeks, including cases with and without gestational diabetes mellitus (GDM). Images were stored in Digital Imaging and Communications in Medicine (DICOM) format, annotated by experts, and converted to segmented masks after quality checks. A balanced dataset was created by randomly excluding overrepresented categories. Artificial intelligence classification models developed using the FastAI library\u2014ResNet\u201018, ResNet\u201034, ResNet\u201050, EfficientNet\u2010B0, and EfficientNet\u2010B7\u2014were trained to detect elevated C\u2010peptide levels (&gt;75th percentile) in umbilical cord blood at birth, based on fetal hepatic ultrasonographic images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Out of 2339 ultrasound images, 606 were excluded due to poor quality, resulting in 1733 images analyzed. Elevated C\u2010peptide levels were observed in 34.3% of neonates. Among the 5 CNN models evaluated, EfficientNet\u2010B0 demonstrated the highest overall performance, achieving a sensitivity of 86.5%, specificity of 82.1%, positive predictive value (PPV) of 83.0%, negative predictive value (NPV) of 85.7%, accuracy of 84.3%, and an area under the ROC curve (AUC) of 0.83 in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI\u2010based analysis of fetal liver echotexture via ultrasound effectively predicted elevated neonatal C\u2010peptide levels, offering a promising non\u2010invasive method for detecting insulin imbalance in newborns.<\/jats:p>\n                  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