DEEP LEARNING IN SPATIOTEMPORAL FETAL CARDIAC IMAGING
Date
2021-08
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Pulchowk Campus
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.
Description
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.
Keywords
Deep Learning,, Fetal Cardiac Cine Loops,, CHD Lesions,, CNN,, RNN,, 3D CNN
Citation
MASTER OF SCIENCE IN INFORMATION AND COMMUNICATION ENGINEERING