AI detection of mild COVID-19 pneumonia from chest CT scans

Abstract

Objectives: An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated.

Methods: In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model.

Results: The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001).

Conclusions: A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test.

Key points: • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.

Keywords: Artificial intelligence; COVID-19; Computer-assisted diagnosis; Deep learning; Volume CT.

Fig. 1

Graphical summary of the utilized deep learning method: (a) the training set and binary labels, (b) the general framework of the 3D CSAC-Net, (c) the testing set with new patients and the model output

Fig. 2

Receiver operating characteristic (ROC) curves: the first panel is the ROC curve of our model for distinguishing mild COVID-19 pneumonia from both mild CAP and NP cases, where S1 is the 213 mild COVID-19 pneumonia scans, S2 is the 162 NP scans and S3 is the 174 mild CAP scans; the second panel shows a comparison of using the 3D ResNet, RF, SVM, and our method to identify mild COVID-19 pneumonia cases with both positive and negative results in initial RT-PCR test, where IRP means the COVID-19 cases with initial RT-PCR positive results and IRN represents the COVID-19 cases with initial RT-PCR negative results.

Fig. 3

Comparison of the heatmaps for different types of cases: a to c are the CT slices of three mild pneumonia COVID-19 cases, where a and b obtained negative results in initial RT-PCR (sample a was confirmed positive by the second test, b was confirmed positive by the third test), d to f are the corresponding heatmaps for a to c, g is the slice of a non-pneumonia case, h and i are CT slices for two mild CAP cases, j to l are the corresponding heatmaps for g to i

Fig. 4

Feature heatmaps of some misdiagnosed cases: a is the CT slice for a misdiagnosed case of COVID-19; b and c are two misdiagnosed CAP cases, d to f are the corresponding heatmaps for a to c