* Correspondence: felix.wegner@ukmuenster.de; Tel.: +49-251-8346762; Fax: +49-251-8349965
Abstract: Introduction: Automated echocardiography image interpretation has the potential to
transform clinical practice. However, neural networks developed in general cohorts may underper-
form in the setting of altered cardiac anatomy. Methods: Consecutive echocardiographic studies
of patients with congenital or structural heart disease (C/SHD) were used to validate an existing
convolutional neural network trained on 14,035 echocardiograms for automated view classification.
In addition, a new convolutional neural network for view classification was trained and tested
specifically in patients with C/SHD. Results: Overall, 9793 imaging files from 262 patients with
C/SHD (mean age 49 years, 60% male) and 62 normal controls (mean age 45 years, 50.0% male) were
included. Congenital diagnoses included among others, tetralogy of Fallot (30), Ebstein anomaly
(18) and transposition of the great arteries (TGA, 48). Assessing correct view classification based on
284,250 individual frames revealed that the non-congenital model had an overall accuracy of 48.3%
for correct view classification in patients with C/SHD compared to 66.7% in patients without cardiac
disease. Our newly trained convolutional network for echocardiographic view detection based on
over 139,910 frames and tested on 35,614 frames from C/SHD patients achieved an accuracy of 76.1%
in detecting the correct echocardiographic view. Conclusions: The current study is the first to validate
view classification by neural networks in C/SHD patients. While generic models have acceptable
accuracy in general cardiology patients, the quality of image classification is only modest in patients
with C/SHD. In contrast, our model trained in C/SHD achieved a considerably increased accuracy
in this particular cohort.
Keywords: deep learning; neural network; structural heart disease; congenital heart disease;
echocardiography
