AI-Driven Fetal Liver Echotexture Analysis: A New Frontier in Predicting Neonatal Insulin Imbalance.

Abstract

Objectives: To evaluate the performance of artificial intelligence (AI)-based models in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis.

Methods: This diagnostic accuracy study analyzed ultrasound images of fetal livers from pregnancies between 37 and 42 weeks, 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-ResNet-18, ResNet-34, ResNet-50, EfficientNet-B0, and EfficientNet-B7-were trained to detect elevated C-peptide levels (>75th percentile) in umbilical cord blood at birth, based on fetal hepatic ultrasonographic images.

Results: Out of 2339 ultrasound images, 606 were excluded due to poor quality, resulting in 1733 images analyzed. Elevated C-peptide levels were observed in 34.3% of neonates. Among the 5 CNN models evaluated, EfficientNet-B0 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.

Conclusion: AI-based analysis of fetal liver echotexture via ultrasound effectively predicted elevated neonatal C-peptide levels, offering a promising non-invasive method for detecting insulin imbalance in newborns.