Quantitative estimation of pulmonary artery wedge pressure from chest radiographs by a regression convolutional neural network.

ABSTRACT: Recent studies reported that a convolutional neural network (CNN; a deep learning model) can detect elevated pulmonary artery wedge pressure (PAWP) from chest radiographs, the diagnostic images most commonly used for assessing pulmonary congestion in heart failure. However, no method has been published for quantitatively estimating PAWP from such radiographs. We hypothesized that a regression CNN, an alternative type of deep learning, could be a useful tool for quantitatively estimating PAWP in cardiovascular diseases. We retrospectively enrolled 936 patients with cardiovascular diseases who had undergone right heart catheterization (RHC) and chest radiography and estimated PAWP by constructing a regression CNN based on the VGG16 model. We randomly categorized 80% of the data as training d

SUBMITTER: Saito Y 

PROVIDER: S-EPMC9239946 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature