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Published on: December 19, 2020
A Cohort Study of Pediatric Severe Community-Acquired Pneumonia Involving AI-Based CT Image Parameters and Electronic
Mengyuan He1, Jianpeng Yuan2, Aijiao Liu1
1Pediatric Hematology Laboratory, Division of Hematology/Oncology, Department, of Pediatrics, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
Insights
Artificial intelligence (AI) analyzing chest CT scans can help predict respiratory failure in children with severe community-acquired pneumonia (CAP). Combining AI imaging data with clinical information improves diagnostic accuracy for better patient outcomes.
Area of Science:
- Pediatric Pulmonology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Community-acquired pneumonia (CAP) poses a significant global health burden in children, marked by high morbidity and mortality.
- Early diagnosis and intervention are critical for improving outcomes in pediatric CAP.
- Artificial intelligence (AI) offers potential for analyzing medical imaging data to enhance precision research and personalized clinical management.
Purpose of the Study:
- To evaluate the predictive ability of AI-derived chest computed tomography (CT) indices for respiratory failure in children with severe CAP.
- To compare the diagnostic performance of AI-derived CT indices alone versus models incorporating clinical and electronic health record data.
Main Methods:
- Retrospective analysis of 230 children hospitalized with severe CAP.
- Patients were categorized based on the presence or absence of respiratory failure.
- Logistic regression and receiver operating characteristic (ROC) curve analysis were employed to assess the predictive capability of AI-derived chest CT indices.
Main Results:
- Increased number of involved lung lobes and bilateral lung involvement were significantly associated with respiratory failure after adjusting for clinical factors.
- Models incorporating electronic health record data alongside AI-derived CT features demonstrated superior discriminatory power compared to models using only CT parameters.
- Model 2 achieved 84.3% sensitivity and 59.8% specificity, while Model 3 achieved 68.6% sensitivity and 76.0% specificity for predicting respiratory failure.
Conclusions:
- AI-derived chest CT indices show promise for high diagnostic accuracy in severe CAP, potentially guiding precise interventions.
- Integrating clinical, laboratory, and AI-derived chest CT indices is crucial for accurate prediction and effective treatment of severe CAP in children.
Introduction:
Community-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.
Methods:
The 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.
Results:
After 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.
Conclusion:
The 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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