Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.

ABSTRACT:

Background

Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes.

Methods

This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories.

Results

Complete da

SUBMITTER: Rezoagli E 

PROVIDER: S-EPMC11301830 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature