Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning.

ABSTRACT: A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamb

SUBMITTER: Kusunose K 

PROVIDER: S-EPMC7277840 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature