ArticleScientific reports2024
Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prevalence, risk factors, and early prediction of refractory pneumonia caused by Mycoplasma pneumoniae in children: a systematic review and meta-analysis.European journal of pediatrics · 2026Pooled it
- Explainable machine learning for diagnosing severeFrontiers in pediatrics · 2026Article
- Early identification of refractoryFrontiers in medicine · 2026Article
- A causal forest model integrating quantitative CT scores to predict benefit from flexible bronchoscopy in pediatric Mycoplasma pneumoniae pneumonia: a two-center retrospective study.Respiratory research · 2025Article
- Innovations in pediatric lung health amid global health shifts.World journal of pediatrics : WJP · 2025Article
- Quantitative analysis of pulmonary vascular alterations in children with refractoryQuantitative imaging in medicine and surgery · 2025Article
- Machine learning models based on chest computed tomography for identifying plastic bronchitis in children withTranslational pediatrics · 2025Article
- Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods.Scientific reports · 2025Article
- Lung consolidation absorption time in 238 pediatric cases of mycoplasma pneumoniae pneumonia.Frontiers in pediatrics · 2025Article
- Development of a radiomics-based model using computed tomography imaging to assess the incidence of extrapulmonary organ involvement inFrontiers in medicine · 2025Article
- Biomarkers associated with the diagnosis and prognosis ofFrontiers in cellular and infection microbiology · 2025Review
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9 authors.
Funding
Abstract
The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant challenge. This study aimed to develop an early predictive model utilizing artificial intelligence (AI)-derived quantitative assessment of lung lesion extent on initial computed tomography (CT) scans and clinical indicators for RMPP in pediatric inpatients. A retrospective cohort study was conducted on patients with M. pneumoniae pneumonia (MP) admitted to the Children's Hospital of Nanjing Medical University, China from January 2019 to December 2020. An early prediction model was developed by stratifying the patients with Mycoplasma pneumoniae pneumonia (MPP) into two cohorts according to the presence or absence of refractory pneumonia. A retrospective cohort of 126 children diagnosed with Mycoplasma pneumoniae pneumonia (MPP) was utilized as a training set, with 85 cases classified as RMPP. Subsequently, a prospective cohort comprising 54 MPP cases, including 37 instances of RMPP, was assembled as a validation set to assess the performance of the predictive model for RMPP from January to December 2021. We defined a constant Φ which can combine the volume and CT value of pulmonary lesions and be further used to calculate the logarithm of Φ to the base of 2 (Log
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