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

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-...

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Vydáno v:	PloS one Ročník 20; číslo 7; s. e0328061
Hlavní autoři:	Enshaei, Nastaran, Mohammadi, Arash, Naderkhani, Farnoosh, Daneman, Nick, Abu Mughli, Rawan, Anconina, Reut, Berger, Ferco H., Kozak, Robert Andrew, Mubareka, Samira, Villanueva Campos, Ana Maria, Narang, Keshav, Vivekanandan, Thayalasuthan, Chan, Adrienne Kit, Lam, Philip, Andany, Nisha, Oikonomou, Anastasia
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 29.07.2025 Public Library of Science (PLoS)
Témata:	Accuracy Adult Aged Automation Biology and Life Sciences Chest Classification Community-Acquired Infections - diagnostic imaging Comparative analysis COVID-19 COVID-19 - diagnosis COVID-19 - diagnostic imaging Datasets Deep Learning Development and progression Diagnosis Diagnosis, Differential Disease transmission Female Health aspects Humans Infections Machine learning Male Medicine and Health Sciences Methods Middle Aged Pandemics People and Places Physical Sciences Pneumonia Pneumonia, Viral - diagnosis Pneumonia, Viral - diagnostic imaging Radiography, Thoracic - methods Research and Analysis Methods SARS-CoV-2 - isolation & purification Severe acute respiratory syndrome coronavirus 2 Severity of Illness Index Subgroups Technology application Viral infections Viral pneumonia Canada
ISSN:	1932-6203, 1932-6203
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Abstract	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. External validation on independent public datasets confirmed the model’s generalizability. Subgroup analyses stratified by patient age, sex, and severity levels further demonstrated consistent performance, supporting the system’s robustness across diverse clinical populations. These findings suggest that the proposed DL system could assist radiologists in the early diagnosis and severity assessment of COVID-19 from baseline CXRs, particularly in resource-limited settings.
AbstractList	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. External validation on independent public datasets confirmed the model's generalizability. Subgroup analyses stratified by patient age, sex, and severity levels further demonstrated consistent performance, supporting the system's robustness across diverse clinical populations. These findings suggest that the proposed DL system could assist radiologists in the early diagnosis and severity assessment of COVID-19 from baseline CXRs, particularly in resource-limited settings. 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. External validation on independent public datasets confirmed the model's generalizability. Subgroup analyses stratified by patient age, sex, and severity levels further demonstrated consistent performance, supporting the system's robustness across diverse clinical populations. These findings suggest that the proposed DL system could assist radiologists in the early diagnosis and severity assessment of COVID-19 from baseline CXRs, particularly in resource-limited settings.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. External validation on independent public datasets confirmed the model's generalizability. Subgroup analyses stratified by patient age, sex, and severity levels further demonstrated consistent performance, supporting the system's robustness across diverse clinical populations. These findings suggest that the proposed DL system could assist radiologists in the early diagnosis and severity assessment of COVID-19 from baseline CXRs, particularly in resource-limited settings.
Audience	Academic
Author	Mohammadi, Arash Daneman, Nick Abu Mughli, Rawan Lam, Philip Andany, Nisha Oikonomou, Anastasia Mubareka, Samira Enshaei, Nastaran Naderkhani, Farnoosh Berger, Ferco H. Vivekanandan, Thayalasuthan Villanueva Campos, Ana Maria Anconina, Reut Narang, Keshav Kozak, Robert Andrew Chan, Adrienne Kit
AuthorAffiliation	1 Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Quebec, Canada 3 Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada Najran University College of Computer Science and Information Systems, SAUDI ARABIA 2 Department of Medicine, Division of Infectious Diseases, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada 4 Biological Sciences Platform, Sunnybrook Research Institute and Shared Hospital Laboratory, Toronto, Canada 5 Department of Microbiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
AuthorAffiliation_xml	– name: 5 Department of Microbiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada – name: 3 Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada – name: 1 Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Quebec, Canada – name: 4 Biological Sciences Platform, Sunnybrook Research Institute and Shared Hospital Laboratory, Toronto, Canada – name: 2 Department of Medicine, Division of Infectious Diseases, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada – name: Najran University College of Computer Science and Information Systems, SAUDI ARABIA
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40729327$$D View this record in MEDLINE/PubMed
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Snippet	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...
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SubjectTerms	Accuracy Adult Aged Automation Biology and Life Sciences Chest Classification Community-Acquired Infections - diagnostic imaging Comparative analysis COVID-19 COVID-19 - diagnosis COVID-19 - diagnostic imaging Datasets Deep Learning Development and progression Diagnosis Diagnosis, Differential Disease transmission Female Health aspects Humans Infections Machine learning Male Medicine and Health Sciences Methods Middle Aged Pandemics People and Places Physical Sciences Pneumonia Pneumonia, Viral - diagnosis Pneumonia, Viral - diagnostic imaging Radiography, Thoracic - methods Research and Analysis Methods SARS-CoV-2 - isolation & purification Severe acute respiratory syndrome coronavirus 2 Severity of Illness Index Subgroups Technology application Viral infections Viral pneumonia
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Title	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
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