Optimizing Screening for Intrauterine Fetal Growth Restriction in Low-Resource Settings Using 2D Ultrasound: A Deep Learning Approach

Severe fetal growth restriction (sFGR) affects 5 to 10% of pregnancies worldwide and is a major contributor to perinatal morbidity and mortality, particularly in low- and middle-income countries (LMICs). Traditional 2D ultrasound detection methods suffer from operator dependency, gestational age uncertainty, and limited access to Doppler in many low-resource facilities. This study presents a deep learning framework for sFGR screening and triage using 2D fetal abdominal ultrasound images designed to operate independently of precise gestational dating. Growth restriction severity labels were derived by mapping abdominal circumference measurements to INTERGROWTH-21st term percentiles as a gestational-age-normalized proxy for fetal size restriction when case-level gestational age or birth-weight data are unavailable. A systematic literature review of 37 studies revealed gaps in severity stratification and generalizability. We implemented a DenseNet-121-based model with abdominal circumference measurement for severity-aware classification using a retrospective single-center dataset of 1588 annotated fetal abdominal images from 169 term pregnancies. Patient-wise 3-fold cross-validation and ensemble testing yielded 93.7% accuracy, a weighted F1-score of 0.76, and ROC AUC [≥] 0.98 per class on heldout data. The approach outperforms previously reported single-center methods on this dataset while explicitly targeting LMIC-specific constraints. It demonstrates potential as a gestational-age-independent first-line triage layer for equitable prenatal screening, subject to prospective multi-site validation.