AI Learns from Ultrasound: Predicting Prenatal Renal Anomalies

This research explores the application of self-supervised learning to medical imaging, potentially improving the detection of prenatal renal anomalies. The use of self-supervised learning could reduce the need for large, labeled datasets, which is often a bottleneck in medical AI development.

• •Applies self-supervised learning to ultrasound imaging for detecting renal anomalies.
• •Aims to reduce the reliance on large labeled datasets for medical AI training.
• •Focuses on prenatal imaging, offering potential benefits for early diagnosis.