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.

Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study

“…The CR images were evaluated using a commercialized DL algorithm capable of analyzing CR images (Lunit INSIGHT for CR 2, Lunit; accessible at https://insight.lunit.io ) that was approved by the Ministry of Food and Drug Safety of Korea [ 7 , 15 ]. The algorithm was developed to detect major thoracic diseases, including pulmonary malignancy, active pulmonary tuberculosis, pneumonia, and pneumothorax [ 7 ].…”

Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019

“…The Korean Society of Thoracic Radiology (KSTR) has been much involved in professional activities designed to address the challenges posed by COVID-19 since February 2020, and also participates in academic research, 1 4 9 10 11 12 13 14 15 16 shares representative cases, 17 18 issues imaging guidelines, 19 and provides COVID-19 education. 20 In addition to these activities, the KSTR recently constructed a nation-wide COVID-19 database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members.…”

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

“… FC-Densenet103, Unet, DenseNet, and DenseNet121-FPN. References References [ 137 ] [ 138 ] [ 139 ] [ 140 ] [ 141 ] [ 142 ] [ 143 ] [ 129 ] [ 144 ] [ 145 ] [ 146 ] [ 147 ] [ 148 ] [ 149 ] [ 150 ] [ 151 ] [ 152 ] [ 153 ] [ 154 ] [ 155 ] [ 156 ] [ 157 ] [ 158 ] [ 159 ] [ 160 ] [ 161 ] [ 162 ] [ 163 ] [ 164 ] [ 165 ] Classification Characteristics Characteristics Gray scale feature extraction and ML classifier, and model-based techniques. Resnet-50, CNN, SVM, ResNet101, VGG16, and VGG19.…”