Abstract 13945: Evaluation of a Deep Neural Network for Detection of D-Transposition of the Great Arteries on Fetal Echocardiograms

Abstract

Introduction: While prenatal detection of congenital heart disease (CHD) has improved over the years, a significant proportion of CHD remains unrecognized on fetal ultrasound. Prenatal identification of d-transposition of the great arteries (d-TGA) in particular would significantly alter delivery planning and postnatal course. Research Questions: We investigated whether a deep neural network (DNN) can accurately detect d-TGA on fetal echocardiograms. Aim: To evaluate a DNN for detection of d-TGA on second (2T) or third (3T) trimester fetal echocardiograms. Methods: Patients with a singleton pregnancy with a 2T or 3T fetal echocardiogram performed between Jan 1, 2022 and May 1, 2023 at one center were retrospectively evaluated. Cases of d-TGA and negative cases referred for family history of CHD or increased nuchal translucency were included. Cases were reviewed by 3 pediatric cardiologists to confirm that the recorded ultrasound clips were sufficient to diagnose the presence or absence of d-TGA, and were excluded otherwise. The DNN was trained on patients not included in the evaluation to detect the absence of the great artery cross-over pattern, a distinct finding in d-TGA. The DNN outputs the presence or absence of this finding using all recorded video clips of an examination. Results: We included 18 positive and 99 negative cases. The DNN detected suspicion for d-TGA with an AUC of 91.4%, sensitivity of 83.3% (60.8-94.2, 95%CI) and specificity of 92.9% (86.1-96.5, 95%CI). Conclusions: D-TGA can be accurately detected on fetal echocardiograms using a DNN. These promising results lay the foundation for reliable, computer-aided detection of CHD on screening fetal ultrasounds.