Automated estimation of total lung volume using chest radiographs and deep learning.

ABSTRACT:

Background

Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases.

Purpose

In this study, we investigate the performance of several deep-learning approaches for automated measurement of total lung volume from chest radiographs.

Methods

About 7621 posteroanterior and lateral view chest radiographs (CXR) were collected from patients with chest CT available. Similarly, 928 CXR studies were chosen from patients with pulmonary function test (PFT) results. The reference total lung volume was calculated from lung segmentation on CT or PFT data, respectively. This dataset was used to train deep-learning architectures to predict total lung volume from chest radiographs. The experiments were constructed in a stepwise

SUBMITTER: Sogancioglu E 

PROVIDER: S-EPMC9545721 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature