Deep learning-based lung volume estimation with dynamic chest radiography.

ABSTRACT:

Background

Dynamic chest radiography (DCR) is a recently developed low-dose pulmonary functional imaging method that can be performed in a general X-ray room. DCR provides sequential images during respiration, and the measured changes in lung area are a promising diagnostic indicator of lung function.

Purpose

To investigate lung volume estimation using deep learning from DCR images during respiration and evaluate its accuracy in comparison with previously proposed estimation methods.

Methods

Two convolutional neural networks (CNNs), VGG19 and DenseNet121, were trained using DCR image datasets from 257 patients, with reference lung volumes derived from corresponding computed tomography (CT) images. The performance of the models was evaluated using mean absolute error (

SUBMITTER: Ishihara N 

PROVIDER: S-EPMC12854853 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature