Differentiation of COVID-19 from other types of viral pneumonia and severity scoring on baseline chest radiographs: Comparison of deep learning with multi-reader evaluation

作者：Nastaran Enshaei, Arash Mohammadi, Farnoosh Naderkhani, Nick Daneman, Rawan Abu Mughli, Reut Anconina, Ferco H. Berger, Robert Kozak, Samira Mubareka, A.M. Villanueva Campos, Keshav Narang, Vivekanandan Thayalasuthan, Adrienne K. Chan, Philip W. Lam, Nisha Andany, Anastasia Oikonomou · 发表于：PLoS ONE · 年份：2025 · DOI：10.1371/journal.pone.0328061 · 被引用次数：4 · 研究领域：COVID-19 diagnosis using AI、Radiology practices and education、Pneumonia and Respiratory Infections

Chest X-ray (CXR) imaging plays a pivotal role in the diagnosis and prognosis of viral pneumonia. However, distinguishing COVID-19 CXRs from other viral infections remains challenging due to highly similar radiographic features. Most existing deep learning (DL) models focus on differentiating COVID-19 from community-acquired pneumonia (CAP) rather than other viral pneumonias and often overlook baseline CXRs, missing the critical window for early detection and intervention. Moreover, manual severity scoring of COVID-19 CXRs by radiologists is subjective and time-intensive, highlighting the need for automated systems. This study introduces a DL system for distinguishing COVID-19 from other viral pneumonias on baseline CXRs acquired within three days of PCR testing, and for automated severity scoring of COVID-19 CXRs. The system was developed using a dataset of 2,547 patients (808 COVID-19, 936 non-COVID viral pneumonia, and 803 normal cases) and validated externally on several publicly accessible datasets. Compared to four experienced radiologists, the model achieved higher diagnostic accuracy (76.4% vs. 71.8%) and enhanced COVID-19 identification (F1-score: 74.1% vs. 61.3%), with an AUC of 93% for distinguishing between viral pneumonia and normal cases, and 89.8% for differentiating COVID-19 from other viral pneumonias. The severity-scoring module exhibited a high Pearson correlation of 93% and a low mean absolute error (MAE) of 2.35 compared to the radiologists' consensus. Ex...