Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT

By Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, B. Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Q. Song and 8 more. 2020

1,846 citations recorded by the source corpus.

Background Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). Conclusions A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.

Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020, neutral): “975 [16] CT 3D No 3,322 4,356 - 1,296‡ 1,735‡ 1,325‡ 427‡ 3D 0.”
COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings (2020, background): “Different AI approaches have recently been proposed to support the management of the pandemic [7, 8, 9, 10].”
Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19 (2021, neutral): “[45] COVID-19 and community-acquired pneumonia (CAP) 4352 CT scans (1292 of COVID-19, 1735 of CAP, and 1325 normal CT scans) The models achieved 90% SV and 96% SF for detection of COVID-19 and 87% SV and 92% SF for detection of CAP”
A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2 (2020, positive): “[20]; they present COVNet to predict COVID-19 from CT scans that have been segmented using U-net [21].”
Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network (2020, positive): “Li, et al. [15] used ResNet50 as the backbone.”
Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, methodology): “Li et al. (2020) suggested the COVNet model and obtain 96% accuracy utilizing a large dataset of 4356 chest CT images of Pneumonia patients, 1296 of which were veriﬁed COVID-19 cases [94].”