Radiol Artif Intell. 2026 Apr 8:e250737. doi: 10.1148/ryai.250737. Online ahead of print.
ABSTRACT
Purpose To develop and validate an anatomy-aware, two-stage, end-to-end deep learning (DL) pipeline for fetal brain abnormality automated detection on standardized second-trimester brain US images. Materials and Methods This retrospective multicenter study included 319 fetal brain images (218 normal, 101 abnormal) between 19+0 and 23+6 weeks of gestation from nine international fetal medicine centers, each with paired standard transventricular and transcerebellar axial plane images acquired during second-trimester US between January 2010 and December 2022. Abnormalities were confirmed by neonatal imaging or autopsy. Images were annotated for six key brain regions by two experienced fetal medicine specialists. An anatomy-aware, two-stage DL pipeline was developed, consisting of a YOLOv5-based object detector followed by a classification network using a Mini-ResNet feature extractor within a HexaNet architecture. The pipeline classified each image as normal or abnormal. Object detection performance was evaluated using mean average precision at an intersection-over-union threshold of 0.5 ([email protected]). Classification performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and F1-score. Results The object detection model achieved a [email protected] of 0.93 (95% CI 0.90, 0.96) on the test dataset. The classification model achieved an AUC of 0.96 (95% CI 0.90, 1.00), a sensitivity of 87% (95% CI 67, 100) [13/15], a specificity of 91% (95% CI 79, 100) [29/32], and an F1-score of 0.84 (95% CI 0.67, 0.96) for distinguishing normal from abnormal fetal brain images. Conclusion The developed model achieved high diagnostic performance for the detection of brain anomalies in routine fetal second-trimester US. ©RSNA, 2026.
PMID:41949456 | DOI:10.1148/ryai.250737
