A comprehensive segmentation of chest X-ray improves deep learning –based WHO radiologically confirmed pneumonia diagnosis in children

ConclusionsThe comprehensive segmentation of CXR could improve deep learning –based pneumonia diagnosis in childhood with a more reasonable WHO’s radiological standardized pneumonia classification instead of conventional dichotomous bacterial pneumonia and viral pneumonia.Clinical relevance statementThe comprehensive segmentation of chest X-ray improves deep learning –based WHO confirmed pneumonia diagnosis in children, laying a strong foundation for the potential inclusion of computer-aided pediatric CXR readings in precise classification of pneumonia and PCV vaccine trials efficacy in children.Key Points• The chest X-ray was comprehensively segmented into six anatomical structures of left lung, right lung, mediastinum, diaphragm, ext-left lung, and ext-right lung.• The comprehensive segmentation improved the three-category classification of primary endpoint pneumonia, other infiltrates, and normal with an increase by around 6% in accuracy, precision, specificity, sensitivity, F1-score, and around 3% in AUC.• The comprehensive segmentation gave rise to a more accurate and interpretable visualization results in capturing the pneumonia lesions.
Source: European Radiology - Category: Radiology Source Type: research