DEEP LEARNING IN SPATIOTEMPORAL FETAL CARDIAC IMAGING

Abstract

Fetal echocardiography is a standard diagnostic tool used to evaluate and monitor fetuses with a compromised cardiovascular system associated with a number of fetal conditions. Deep learning is a computer technology which can perform specific tasks with specific goals. Deep learning techniques is used to evaluate fetal cardiac ultrasound cine loops and improve the evaluation of fetal abnormalities. In this study, I implemented convolutional neural network and recurrent neural network as CNN+LSTM, CNN+GRU and 3DCNN, deep learning models for the processing and classification of ultrasonographic cine loops into various classes. The CNN+LSTM, CNN+GRU, and 3D CNN algorithms were able to sort the fetal cardiac cine loops into 5 standard views with 92.63%, 94.99%, and 82.69% accuracy, respectively. Furthermore, the CNN+LSTM, CNN+GRU, and 3D CNN were able to accurately diagnose Tricuspid atresia (TA) and Hypoplastic left heart syndrome (HLHS) with 94.61%, 91.99%, and 86.54%, respectively. These deep learning-based algorithms found to be an effective tool for evaluating and monitoring normal and abnormal fetal heart cine loops.