Abstract

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Title:: Diagnostic value of deep neural network model based on characteristics of density distribution in COVID-19

Author(s):: LI Wen1; HAN Dong2; GUO Youmin3; REN Zhuanqin1; TIAN Hongzhe1; 1.Department of Radiology, Baoji Central Hospital, Baoji 721008, China 2. Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712021, China 3. Department of Radiology, the First Affiliated Hospital of Xian Jiaotong University, Xian 710061, China

Keywords:: Keywords: corona virus disease 2019 characteristic of density distribution CT imaging feature deep neural network

Abstract:: Abstract: Objective To evaluate the efficacy of a deep neural network (DNN) model based on characteristics of density distribution (CDD) in diagnosing corona virus disease 2019 (COVID-19). Methods A total of 42 cases of COVID-19 and 43 cases of non-COVID-19 pneumonia were enrolled in the study. The 211 chest CT images of these patients were divided into a training set (n=128) and a validation set (n=83). Referring to the CT structured report of COVID-19-related pneumonia issued by Radiological Society of North America, the CT imaging features (CTIF) based DNN model (DNN-CTIF) was constructed. Meanwhile, the DNN-CDD model was constructed based on the pneumonia CDD in the chest CT images. ROC curve analysis and decision curve analysis were used to evaluate the diagnostic performances of the two models. Results The AUC of DNN-CTIF model and DNN-CDD model was 0.927 and 0.965 in training set. The AUC of DNN-CDD model in validation set was significantly higher than that of DNN-CTIF model (0.829 vs 0.929, P=0.047). Moreover, the decision curve analysis showed that DNN-CDD model provided more net benefit than DNN-CTIF model in the range of 0.04-1.00 probability threshold.Conclusion Both DNN-CTIF and DNN-CDD models have good diagnostic performance for COVID-19, and DNN-CDD model is superior to DNN-CTIF model.