Generation of short-term follow-up chest CT images using a latent diffusion model in COVID-19.

ABSTRACT:

Purpose

Despite a global decrease in the number of COVID-19 patients, early prediction of the clinical course for optimal patient care remains challenging. Recently, the usefulness of image generation for medical images has been investigated. This study aimed to generate short-term follow-up chest CT images using a latent diffusion model in patients with COVID-19.

Materials and methods

We retrospectively enrolled 505 patients with COVID-19 for whom the clinical parameters (patient background, clinical symptoms, and blood test results) upon admission were available and chest CT imaging was performed. Subject datasets (n = 505) were allocated for training (n = 403), and the remaining (n = 102) were reserved for evaluation. The image underwent variational autoencoder (VAE) enco

SUBMITTER: Kawata N 

PROVIDER: S-EPMC11953082 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature