Deep learning for fetal diaphragmatic hernia detection in ultrasound

Abstract

Congenital diaphragmatic hernia (CDH) is a rare but severe fetal malformation associated with high neonatal morbidity and mortality, where early detection is critical for risk stratification, parental counseling, and perinatal management. Although ultrasound is the primary imaging modality in prenatal screening, CDH detection remains challenging due to limited acoustic windows, overlapping anatomical structures, and strong operator dependence.

The initial objective of this project was to develop deep learning–based methods for automated CDH detection in fetal ultrasound. Due to changes in the clinical collaboration context, the project could not be continued in its originally planned data-driven form. As a result, the focus shifted toward consolidating the existing evidence base and systematically analyzing methodological and clinical barriers for AI-assisted fetal ultrasound diagnostics.

As a central outcome, we co-authored a comprehensive systematic review on artificial intelligence in obstetric and gynecologic ultrasound, critically assessing methodological rigor, data availability, validation practices, and clinical readiness across 189 published studies. The review identified fetal malformation detection, including CDH, as a clinically high-impact yet underexplored application area, highlighting the need for standardized datasets, multi-center validation, and interpretable AI approaches.

Perspective

The insights gained from this project provided a methodological foundation for subsequent ultrasound- and MRI-based research with sustained clinical partnerships and informed the design of later projects with improved data availability and validation strategies. Building on this groundwork, renewed efforts toward CDH detection are envisaged within a national multi-center collaboration, leveraging harmonized data collection and shared clinical expertise.