Pediatric Infectious Diseases: Open Access

Abstract

To evaluate the performance of an automatic unified model that combined a pre-trained deep learning segmentation model, radiomic feature extraction and machine learning methods for classifying coronavirus disease 2019 (COVID19) versus community acquired pneumonia (CAP) in children based on computed tomography (CT). Method: This retrospective study included children with COVID-19 (n = 34) and CAP (n = 70). The CT scans were collected from two children hospitals in China. A pre-trained deep learning segmentation model was used to segment pneumonia lesion on which the radiomic features were extracted. Four classifiers: logistic regression (LR), K nearest neighbours (KNN), random forest (RF) and support vector machine (SVM) were trained and evaluated with leave-one-out cross-validation approach and diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity and accuracy.