aceA CT-based deep learning model for the automated risk stratification of refractory Mycoplasma pneumoniae pneumonia in children
The accurate identification of children with refractory Mycoplasma pneumoniae pneumonia (RMPP) remains challenging. This study aimed to develop a transformer-based model utilizing clinically indicated chest computed tomography (CT) to stratify pediatric RMPP risk at a critical decision point. Non-contrast chest CT data from a multicenter retrospective cohort of 1224 pediatric patients with Mycoplasma pneumoniae pneumonia who underwent clinically indicated CT were used to develop a transformer-based deep learning…
A transformer-based deep learning framework using clinically indicated non-contrast chest CT stratified risk of refractory Mycoplasma pneumoniae pneumonia in children with AUCs around 0.89-0.90 on internal and external test cohorts.
Model was developed and tested only in a retrospective cohort of children who had already undergone clinically indicated chest CT, limiting generalizability to broader screening.
Evidence
- Peer-reviewedBMC Medical Imaging2026-07-17
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Truvace Impact Record TRV-2026-0285, v1: “A CT-based deep learning model for the automated risk stratification of refractory Mycoplasma pneumoniae pneumonia in children.” Truvace, 2026-07-19. /record/TRV-2026-0285 (accessed at citation time). sha256 3ea3847461950a5e…
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