ArticleInfectious diseases and therapy2025
A Cohort Study of Pediatric Severe Community-Acquired Pneumonia Involving AI-Based CT Image Parameters and Electronic Health Record Data.
Article in Infectious diseases and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction.Scientific reports · 2026Article
- Artificial intelligence in the diagnosis and prognosis of pediatric bacterial pneumonia: current advances and challenges.Current opinion in pediatrics · 2026Review
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11 authors.
Funding
Abstract
introductionCommunity-acquired pneumonia (CAP) is a significant concern for children worldwide and is associated with a high morbidity and mortality. To improve patient outcomes, early intervention and accurate diagnosis are essential. Artificial intelligence (AI) can mine and label imaging data and thus may contribute to precision research and personalized clinical management.
methodsThe baseline characteristics of 230 children with severe CAP hospitalized from January 2023 to October 2024 were retrospectively analyzed. The patients were divided into two groups according to the presence of respiratory failure. The predictive ability of AI-derived chest CT (computed tomography) indices alone for respiratory failure was assessed via logistic regression analysis. ROC (receiver operating characteristic) curves were plotted for these regression models.
resultsAfter adjusting for age, white blood cell count, neutrophils, lymphocytes, creatinine, wheezing, and fever > 5 days, a greater number of involved lung lobes [odds ratio 1.347, 95% confidence interval (95% CI) 1.036-1.750, P = 0.026] and bilateral lung involvement (odds ratio 2.734, 95% CI 1.084-6.893, P = 0.033) were significantly associated with respiratory failure. The discriminatory power (as measured by the area under curve) of Model 2 and Model 3, which included electronic health record data and the accuracy of CT imaging features, was better than that of Model 0 and Model 1, which contained only the chest CT parameters. The sensitivity and specificity of Model 2 at the optimal critical value (0.441) were 84.3% and 59.8%, respectively. The sensitivity and specificity of Model 3 at the optimal critical value (0.446) were 68.6% and 76.0%, respectively.
conclusionThe use of AI-derived chest CT indices may achieve high diagnostic accuracy and guide precise interventions for patients with severe CAP. However, clinical, laboratory, and AI-derived chest CT indices should be included to accurately predict and treat severe CAP.
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