  Echocardiography (echo) is an indispensable tool in a cardiologist's
diagnostic armamentarium. To date, almost all echocardiographic parameters
require time-consuming manual labeling and measurements by an experienced
echocardiographer and exhibit significant variability, owing to the noisy and
artifact-laden nature of echo images. For example, mitral inflow (MI) Doppler
is used to assess left ventricular (LV) diastolic function, which is of
paramount clinical importance to distinguish between different cardiac
diseases. In the current work we present a fully automated workflow which
leverages deep learning to a) label MI Doppler images acquired in an echo
study, b) detect the envelope of MI Doppler signal, c) extract early and late
filing (E and A wave) flow velocities and E-wave deceleration time from the
envelope. We trained a variety of convolutional neural networks (CNN) models on
5544 images of 140 patients for predicting 24 image classes including MI
Doppler images and obtained overall accuracy of 0.97 on 1737 images of 40
patients. Automated E and A wave velocity showed excellent correlation (Pearson
R 0.99 and 0.98 respectively) and Bland Altman agreement (mean difference 0.06
and 0.05 m/s respectively and SD 0.03 for both) with the operator measurements.
Deceleration time also showed good but lower correlation (Pearson R 0.82) and
Bland-Altman agreement (mean difference: 34.1ms, SD: 30.9ms). These results
demonstrate feasibility of Doppler echocardiography measurement automation and
the promise of a fully automated echocardiography measurement package.
