COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system

Abstract

Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment where expert radiologists are not available. However, the performance of existing CAD for identifying COVID-19 and associated pneumonia on CXRs has been scarcely investigated. In this study, CXRs of patients with and without COVID-19 confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively collected from four and one institution, respectively, and a commercialized, regulatory-approved CAD that can identify various abnormalities including pneumonia was used to analyze each CXR. Performance of the CAD was evaluated using area under the receiver operating characteristic curves (AUCs), with reference standards of the RT-PCR results and the presence of findings of pneumonia on chest CTs obtained within 24 hours from the CXR. For comparison, 5 thoracic radiologists and 5 non-radiologist physicians independently interpreted the CXRs. Afterward, they re-interpreted the CXRs with corresponding CAD results. The performance of CAD (AUCs, 0.714 and 0.790 against RT-PCR and chest CT, respectively hereinafter) were similar with those of thoracic radiologists (AUCs, 0.701 and 0.784), and higher than those of non-radiologist physicians (AUCs, 0.584 and 0.650). Non-radiologist physicians showed significantly improved performance when assisted with the CAD (AUCs, 0.584 to 0.664 and 0.650 to 0.738). In addition, inter-reader agreement among physicians was also improved in the CAD-assisted interpretation (Fleiss’ kappa coefficient, 0.209 to 0.322). In conclusion, radiologist-level performance of the CAD in identifying COVID-19 and associated pneumonia on CXR and enhanced performance of non-radiologist physicians with the CAD assistance suggest that the CAD can support physicians in interpreting CXRs and helping image-based triage of COVID-19 patients in resource-constrained environment.

Development and Validation of a Multimodal-Based Prognosis and Intervention Prediction Model for COVID-19 Patients in a Multicenter Cohort

“…The DLAD-10 has previously shown to have good performance in detecting the 10 clinically well-defined and important abnormalities, with AUROCS ranging 0.895-1.00 in CT-confirmed datasets [26]. Furthermore, the engine has shown good performance in COVID-19 patients [42][43][44]. The performance of the DLAD-10 is high because it has been trained and validated on large datasets, utilizing over three million images for pretraining and fine-tuning.…”

A Promising and Challenging Approach: Radiologists’ Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19

“…The authors believed that DeepCOVID-XR is more reliable than the study by Murphy et al for predictions produced by DeepCOVID-XR are in line with the radiological diagnosis by a consensus of experts. Hwang et al [27] observed that DL-based CAD may improve the performance of readers, while the performance of nonradiologists significantly improved in the CADassisted interpretation. Furthermore, inter-reader agreement among physicians showed significant improvement when assisted with the CAD.…”

Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs

“…To increase the sensitivity and specificity of imaging patterns for pneumonia in CR, deep learning (DL) algorithms must become more prevalent. Prior studies have shown that the use of artificial intelligence (AI) significantly improves the detection of pneumonia in CR [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. However, the number of relevant studies is comparatively low [ 11 , 16 ].…”

“…Prior studies have shown that the use of artificial intelligence (AI) significantly improves the detection of pneumonia in CR [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. However, the number of relevant studies is comparatively low [ 11 , 16 ].…”