A rapid screening classifier for diagnosing COVID-19
Yang Xia, Weixiang Chen, Hongyi Ren, Jianping Zhao, Lihua Wang, Rui Jin, Jie-Sen Zhou, Qiyuan Wang, Fugui Yan, Bin Zhang, Jian Lou, Shaobin Wang, Xiaomeng Li, Jie Zhou, Liming Xia, Cheng Jin, Jianjiang Feng, Wen Li, Huahao Shen
Abstract
Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction.
Topics & Concepts
MedicineCoronavirus disease 2019 (COVID-19)Receiver operating characteristicClassifier (UML)Artificial intelligencePneumoniaRadiologyInternal medicineDiseaseInfectious disease (medical specialty)Computer scienceCOVID-19 diagnosis using AICOVID-19 Clinical Research StudiesPhonocardiography and Auscultation Techniques