Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
Department of Radiology, Seoul National University Hospital, Seoul, Korea.
PLoS One. 2021 Jun 7;16(6):e0252440. doi: 10.1371/journal.pone.0252440. eCollection 2021.
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.
胸部 X 光片（CXR）可以帮助资源有限环境下的冠状病毒病（COVID-19）患者进行分诊，而能够在 CXR 上识别肺炎的计算机辅助检测系统（CAD）可能有助于在没有专家放射科医生的情况下对这些环境中的患者进行分诊。然而，现有的 CAD 用于识别 CXR 上的 COVID-19 和相关肺炎的性能尚未得到充分研究。在这项研究中，分别从四个和一个机构回顾性收集了经逆转录酶聚合酶链反应（RT-PCR）证实患有 COVID-19 和不患有 COVID-19 的患者的 CXR，使用一种商业化的、经监管部门批准的 CAD，该 CAD 可以识别包括肺炎在内的各种异常情况，用于分析每张 CXR。使用受试者工作特征曲线下的面积（AUCs）评估 CAD 的性能，参考标准为 RT-PCR 结果和从 CXR 获得的 24 小时内胸部 CT 上肺炎的存在。为了比较，5 名胸部放射科医生和 5 名非放射科医生独立解释了 CXR。之后，他们根据相应的 CAD 结果重新解释了 CXR。CAD 的性能（AUCs，分别针对 RT-PCR 和胸部 CT，为 0.714 和 0.790）与放射科医生的性能（AUCs，分别为 0.701 和 0.784）相似，且高于非放射科医生的性能（AUCs，分别为 0.584 和 0.650）。当使用 CAD 辅助时，非放射科医生的表现明显提高（AUCs，分别从 0.584 提高到 0.664 和从 0.650 提高到 0.738）。此外，CAD 辅助解释还提高了医生之间的读者间一致性（Fleiss' kappa 系数，从 0.209 提高到 0.322）。总之，CAD 在识别 CXR 上的 COVID-19 和相关肺炎方面达到了放射科医生的水平，并且 CAD 辅助提高了非放射科医生的性能，这表明 CAD 可以支持医生解释 CXR，并帮助资源有限环境下的 COVID-19 患者进行基于图像的分诊。
