Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy
Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, Kunlin Cao, Daliang Liu, Guisheng Wang, Qizhong Xu, Xisheng Fang, Shiqin Zhang, Juan Xia, Jun Xia
Abstract
< .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020
Topics & Concepts
MedicineReceiver operating characteristicCoronavirus disease 2019 (COVID-19)PneumoniaConfidence intervalRadiologyCommunity-acquired pneumoniaArea under the curveNuclear medicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Area under curveComputed tomographyInternal medicineDiseaseInfectious disease (medical specialty)PharmacokineticsCOVID-19 diagnosis using AICOVID-19 Clinical Research StudiesRadiomics and Machine Learning in Medical Imaging