Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning.

ABSTRACT:

Objective

Rheumatic heart disease (RHD) affects an estimated 39 million people worldwide and is the most common acquired heart disease in children and young adults. Echocardiograms are the gold standard for diagnosis of RHD, but there is a shortage of skilled experts to allow widespread screenings for early detection and prevention of the disease progress. We propose an automated RHD diagnosis system that can help bridge this gap.

Materials and methods

Experiments were conducted on a dataset with 11 646 echocardiography videos from 912 exams, obtained during screenings in underdeveloped areas of Brazil and Uganda. We address the challenges of RHD identification with a 3D convolutional neural network (C3D), comparing its performance with a 2D convolutional neural network (VGG

SUBMITTER: Martins JFBS 

PROVIDER: S-EPMC8363807 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature