Development and validation of a machine learning model for predicting co-infection of <i>Mycoplasma pneumonia</i> in pediatric patients.

ABSTRACT:

Background

Mycoplasma pneumoniae pneumonia (MPP) is endemic in China, while Mycoplasma co-infection with other pathogens (Co-MPP) linked to severe outcomes. Despite radiomics and machine learning potential in pneumonia, pediatric Co-MPP differentiation remains underexplored. This study aimed to bridge this gap by evaluating machine learning models, particularly radiomics features derived from high-resolution computed tomography (HRCT) scans, to differentiate between MPP and Co-MPP, and to compare their predictive performance with traditional clinical models.

Methods

We conducted a retrospective analysis of hospitalized pediatric pneumonia patients from June to December 2023 at Affiliated Hospital of Xuzhou Medical University. Chest computed tomography (CT) scan

SUBMITTER: Liu X 

PROVIDER: S-EPMC12268717 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature