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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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0328061 |
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| author | Nastaran Enshaei Arash Mohammadi Farnoosh Naderkhani Nick Daneman Rawan Abu Mughli Reut Anconina Ferco H Berger Robert Andrew Kozak Samira Mubareka Ana Maria Villanueva Campos Keshav Narang Thayalasuthan Vivekanandan Adrienne Kit Chan Philip Lam Nisha Andany Anastasia Oikonomou |
| author_facet | Nastaran Enshaei Arash Mohammadi Farnoosh Naderkhani Nick Daneman Rawan Abu Mughli Reut Anconina Ferco H Berger Robert Andrew Kozak Samira Mubareka Ana Maria Villanueva Campos Keshav Narang Thayalasuthan Vivekanandan Adrienne Kit Chan Philip Lam Nisha Andany Anastasia Oikonomou |
| author_sort | Nastaran Enshaei |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b3742be7f8204ad6982e8a013f42d059 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b3742be7f8204ad6982e8a013f42d0592025-08-20T03:23:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032806110.1371/journal.pone.0328061Differentiation of COVID-19 from other types of viral pneumonia and severity scoring on baseline chest radiographs: Comparison of deep learning with multi-reader evaluation.Nastaran EnshaeiArash MohammadiFarnoosh NaderkhaniNick DanemanRawan Abu MughliReut AnconinaFerco H BergerRobert Andrew KozakSamira MubarekaAna Maria Villanueva CamposKeshav NarangThayalasuthan VivekanandanAdrienne Kit ChanPhilip LamNisha AndanyAnastasia OikonomouChest 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.https://doi.org/10.1371/journal.pone.0328061 |
| spellingShingle | Nastaran Enshaei Arash Mohammadi Farnoosh Naderkhani Nick Daneman Rawan Abu Mughli Reut Anconina Ferco H Berger Robert Andrew Kozak Samira Mubareka Ana Maria Villanueva Campos Keshav Narang Thayalasuthan Vivekanandan Adrienne Kit Chan Philip Lam Nisha Andany Anastasia Oikonomou 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. PLoS ONE |
| 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. |
| title_full | 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. |
| title_fullStr | 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. |
| title_full_unstemmed | 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. |
| title_short | 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. |
| title_sort | 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 |
| url | https://doi.org/10.1371/journal.pone.0328061 |
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