Back to search

Article

Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation Study

2025-08-01

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Background</h4> Approximately 70% of adults with chronic obstructive pulmonary disease (COPD) remain undiagnosed. Opportunistic screening using chest computed tomography (CT) scans, commonly acquired in clinical practice, may be used to improve COPD detection through simple, clinically applicable deep-learning models. We developed a lightweight, convolutional neural network (COPDxNet) that...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
6b298442-f8ac-5eb9-b288-f220a19bed35
DOI
10.1101/2025.07.30.25332459
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation StudyDOI 10.1101/2025.07.30.25332459
Select a neighboring publication to make it the new centre.