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An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest
Mudan Zhang1,2, Siwei Yu2,3, Xuntao Yin4,5
1Guizhou University, School of Medicine, Guiyang, 550000, Guizhou Province, China.
Insights
This study developed an AI model using radiomics and CT scans to classify bacterial pneumonia types in children. The model shows potential for aiding in differential diagnosis to guide empirical antibiotic therapy (EAT).
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- Bacterial pneumonia in children presents diagnostic challenges.
- Accurate classification is crucial for effective empirical antibiotic therapy (EAT).
- Radiomics offers potential for non-invasive disease characterization.
Purpose of the Study:
- To develop an artificial intelligence-based radiomics multi-class classification model.
- To differentiate between Gram-positive, Gram-negative, and atypical bacterial pneumonia in children.
- To support empirical antibiotic therapy (EAT) decisions using low-dose chest CT images.
Main Methods:
- Retrospective analysis of 389 pediatric bacterial pneumonia cases (Gram-positive, Gram-negative, atypical).
- Extraction of radiomics features from low-dose chest CT images.
- Development and integration of three optimal machine-learning submodels into a comprehensive classification model.
Main Results:
- A radiomics multi-class classification model was constructed using five selected features.
- The support vector machine-based model achieved an average AUC of 0.75 and ACC of 0.58 in the training set.
- The model demonstrated an average AUC of 0.73 and ACC of 0.54 in the test set, indicating potential utility.
Conclusions:
- The developed radiomics multi-class classification model shows promise as an auxiliary tool.
- Further research is warranted for its application in the differential diagnosis of pediatric bacterial pneumonias.
- This AI-driven approach could enhance the precision of empirical antibiotic therapy (EAT).
Purpose:
To construct an auxiliary empirical antibiotic therapy (EAT) multi-class classification model for children with bacterial pneumonia using radiomics features based on artificial intelligence and low-dose chest CT images.
Materials And Methods:
Data were retrospectively collected from children with pathogen-confirmed bacterial pneumonia including Gram-positive bacterial pneumonia (122/389, 31%), Gram-negative bacterial pneumonia (159/389, 41%) and atypical bacterial pneumonia (108/389, 28%) from January 1 to June 30, 2019. Nine machine-learning models were separately evaluated based on radiomics features extracted from CT images; three optimal submodels were constructed and integrated to form a multi-class classification model.
Results:
We selected five features to develop three radiomics submodels: a Gram-positive model, a Gram-negative model and an atypical model. The comprehensive radiomics model using support vector machine method yielded an average area under the curve (AUC) of 0.75 [95% confidence interval (CI), 0.65-0.83] and accuracy (ACC) of 0.58 [sensitivity (SEN), 0.57; specificity (SPE), 0.78] in the training set, and an average AUC of 0.73 (95% CI 0.61-0.79) and ACC of 0.54 (SEN, 0.52; SPE, 0.75) in the test set.
Conclusion:
This auxiliary EAT radiomics multi-class classification model was deserved to be researched in differential diagnosing bacterial pneumonias in children.
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Bacterial Pneumonia Treatment
For bacterial pneumonia, antibiotics serve as the cornerstone of therapy. Initial treatment often begins with empirical antibiotics, tailored to the anticipated causative organism and adjusted based on culture results. Key antibiotic choices include: