Deep Learning Approaches to Identify Subtle Anomalies in Prenatal Ultrasound Imaging

Abstract

This research investigated deep learning approaches for detecting subtle anomalies in prenatal ultrasound imaging. Congenital anomalies affect approximately 6% of births worldwide, with detection rates for subtle defects varying significantly based on operator expertise. A multi-institutional dataset comprising 12,450 prenatal ultrasound examinations from three tertiary care centres was employed to develop and evaluate multiple deep learning architectures, including modified convolutional neural networks, generative adversarial networks, autoencoders, and feature fusion approaches. The ensemble approach, which combines these architectures, achieved an overall accuracy of 91.4% and 89.8% accuracy for subtle anomalies, specifically substantially exceeding previous benchmarks. Feature visualisation confirmed that models focused on anatomically appropriate regions when making predictions. Performance varied across anomaly categories, with cardiac defects presenting the most significant challenges. The research identified meaningful relationships between model confidence and clinical significance, with higher sensitivity for anomalies requiring immediate intervention. Expert evaluation confirmed that models occasionally detected subtle findings that were missed during routine interpretation, suggesting a potential complementary role between automated systems and human expertise. The findings demonstrate significant progress toward addressing the challenges of subtle anomaly detection in prenatal ultrasound while identifying important directions for future refinement.