Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19

Abstract

Objective: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance.

Materials and methods: In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated.

Results: Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001).

Conclusion: Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.

Keywords: COVID-19; COVID-19 diagnostic testing; Deep learning; Pneumonia; Radiography, thoracic.

Conflict of interest statement

Eui Jin Hwang, Hyungjin Kim, and Chang Min Park report research grants from the Lunit Inc., outside the present study. Jin Mo Goo report research grant from the INFINITT Healthcare, outside the present study.

Copyright © 2020 The Korean Society of Radiology.

Figures

Fig. 1. Representative case of COVID-19 with true positive CXR.

A. CXR of patient with COVID-19 showing diffuse bilateral pulmonary opacities. B. Computer-aided detection system classified CXR as abnormal with probability score of 86%, with localization of increased opacities in both lungs. C. Formal radiology report suggested that opacities were likely indicative of pneumonia. Chest computed tomography image obtained on same day shows multifocal patchy ground-glass opacities in bilateral peripheral lungs. COVID-19 = coronavirus disease, CXR = chest X-ray radiograph

Fig. 2. Representative case of COVID-19 with false negative CXR.

A. CXR of patient with COVID-19 shows no definite pulmonary opacity. B, C. Computer-aided detection system classified CXR as normal, with probability score below 15% (threshold for visualization). Formal radiology report indicated no abnormal finding on CXR. Chest computed tomography images obtained on same day show multifocal patchy consolidations and ground-glass opacities in bilateral lungs.

Fig. 3. Representative case of false positive identification on CXR.

A. CXR of patient with negative real-time reverse transcriptase-polymerase chain reaction result shows increased opacities at both lower lung fields. B. Computer-aided detection system classified CXR as abnormal with probability score of 63%, and localized opacities in both lower lung fields. C, D. Formal radiology report indicated presence of parenchymal infiltration at right lower lung and possibility of pneumonia. Chest computed tomography images obtained on same day exhibited bilateral pleural effusion, without relevant parenchymal abnormality.