Prediction of Pulmonary to Systemic Flow Ratio in Patients With Congenital Heart Disease Using Deep Learning-Based Analysis of Chest Radiographs.

ABSTRACT:

Importance

Chest radiography is a useful noninvasive modality to evaluate pulmonary blood flow status in patients with congenital heart disease. However, the predictive value of chest radiography is limited by the subjective and qualitive nature of the interpretation. Recently, deep learning has been used to analyze various images, but it has not been applied to analyzing chest radiographs in such patients.

Objective

To develop and validate a quantitative method to predict the pulmonary to systemic flow ratio from chest radiographs using deep learning.

Design, setting, and participants

This retrospective observational study included 1031 cardiac catheterizations performed for 657 patients from January 1, 2005, to April 30, 2019, at a tertiary center. Catheterizations

SUBMITTER: Toba S 

PROVIDER: S-EPMC6990846 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature