Development and validation of a pneumonia severity prediction model using AI analysis of abdominal and paravertebral intramuscular fat on chest CT in COVID-19 patients.

ABSTRACT:

Background

Pneumonia severity assessment is critical for clinical intervention, yet traditional methods often lack precision. This study addresses the clinical need for noninvasive, accurate tools by evaluating artificial intelligence (AI)-driven analysis of chest computed tomography (CT) scans to predict pneumonia severity and disease progression in coronavirus disease 2019 (COVID-19) patients, leveraging body composition metrics.

Methods

This retrospective study included 314 COVID-19 patients who underwent chest CT scans. Inclusion criteria were confirmed COVID-19 diagnosis and availability of CT imaging. Pneumonia severity, determined as the gold standard by expert radiologists assessing lesion percentage on CT, was categorized into Grade 1 (1-24%) and Grades 2-4 (≥25%).

SUBMITTER: Lin B 

PROVIDER: S-EPMC12603512 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature