Development of an Integrated Deep Learning Approach for Detecting Fetal Brain Abnormalities in Routine Second Trimester Ultrasound Scan: A Multicenter Study.
Authors
Affiliations (14)
Affiliations (14)
- Department of Medicine and Surgery, Obstetrics and Gynaecology Unit, University of Parma, Parma, Italy.
- Division of Clinical Epidemiology, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.
- Department Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Street Address, 00168 Rome, Italy.
- Fetal Medicine Unit di Venere and Sarcone Hospitals ASL, Bari, Italy.
- St George's University Hospitals NHS Foundation Trust, London, London, United Kingdom.
- Hospital St Joseph/I.M.R, Marseille, France.
- Obstetrics and Gynecology, University of Verona, Verona, Italy.
- Department of Obstetrics and Gynecology, Division of Obstetrics and Feto-Maternal, Medicine, Medical University of Vienna, Vienna, Austria.
- Azienda Socio Sanitaria Territoriale di Mantua, Mantua, Italy.
- Universita degli Studi di Roma Tor Vergata, Rome, Italy.
- Department of Maternal Fetal Health and Urological Sciences, University Rome Sapienza, Rome, Italy.
- Radiomics GSTeP Core Research Facility, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
- Department of Diagnostic Imaging and Radiation Oncology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
- Università Cattolica del Sacro Cuore, Rome, Italy.
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.