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-...

Celý popis

Uloženo v:

Podrobná bibliografie
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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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
Author_xml	– sequence: 1 givenname: Nastaran surname: Enshaei fullname: Enshaei, Nastaran – sequence: 2 givenname: Arash surname: Mohammadi fullname: Mohammadi, Arash – sequence: 3 givenname: Farnoosh surname: Naderkhani fullname: Naderkhani, Farnoosh – sequence: 4 givenname: Nick surname: Daneman fullname: Daneman, Nick – sequence: 5 givenname: Rawan surname: Abu Mughli fullname: Abu Mughli, Rawan – sequence: 6 givenname: Reut surname: Anconina fullname: Anconina, Reut – sequence: 7 givenname: Ferco H. orcidid: 0000-0003-3696-2554 surname: Berger fullname: Berger, Ferco H. – sequence: 8 givenname: Robert Andrew surname: Kozak fullname: Kozak, Robert Andrew – sequence: 9 givenname: Samira surname: Mubareka fullname: Mubareka, Samira – sequence: 10 givenname: Ana Maria surname: Villanueva Campos fullname: Villanueva Campos, Ana Maria – sequence: 11 givenname: Keshav surname: Narang fullname: Narang, Keshav – sequence: 12 givenname: Thayalasuthan surname: Vivekanandan fullname: Vivekanandan, Thayalasuthan – sequence: 13 givenname: Adrienne Kit surname: Chan fullname: Chan, Adrienne Kit – sequence: 14 givenname: Philip surname: Lam fullname: Lam, Philip – sequence: 15 givenname: Nisha surname: Andany fullname: Andany, Nisha – sequence: 16 givenname: Anastasia orcidid: 0000-0001-6996-237X surname: Oikonomou fullname: Oikonomou, Anastasia
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40729327$$D View this record in MEDLINE/PubMed
BookMark	eNqNk9tu1DAQhiNURA_wBggsISG42MWOvXHCDaq2HFaqVAmq3lqTeLzrKrGDnSz0nXhIvN1t1UW9QLlINPPNP54_nuPswHmHWfaS0Snjkn249mNw0E77FJ5Snpe0YE-yI1bxfFLklB88-D7MjmO8pnTGy6J4lh0KKvOUk0fZnzNrDAZ0g4XBeke8IfOLq8XZhFXEBN8RP6wwkOGmx7hJrm2AlvQOx847CwScJhHXGOxwQ2Ljg3VLknRqiNhah6RZYRxIAG39MkC_ih_J3Hc9BBu37TRiT1qE4Dalv-ywIt3YDnYSEHRqjWtox9vDPc-eGmgjvti9T7LLL58v598m5xdfF_PT80lTCDFMOMu5wZKJHCg0jGqGRssZL5iEGqhumNS6LksQBqu8garmZcOxKnNalMLwk-z1VrZvfVQ7n6PiORcFK2g5S8RiS2gP16oPtoNwozxYdRvwYakgDLZpUdVcirxGaZK6AF2kLlgCZdyIXNNZlbQ-7bqNdYe6Sb8iObwnup9xdqWWfq3SlLSQUiSFdzuF4H-OyW3V2dhg24JDP24PLiktRZHQN_-gj4-3o5aQJrDO-NS42Yiq01JURSVzKRM1fYRKj8bONulWGpviewXv9woSM-DvYQljjGrx4_v_sxdX--zbB-wKoR1W0bfj5srEffDVQ6vvPb5bhwSILdAEH2NAc48wqjZbd2eX2myd2m0d_wv-ziEb
Cites_doi	10.1016/j.eswa.2023.121724 10.1038/s41598-023-47038-3 10.1148/radiol.2020201874 10.1148/ryct.2020200210 10.1038/s41598-023-46126-8 10.1016/j.inffus.2021.04.008 10.3390/tomography8060235 10.1016/j.bpa.2020.11.009 10.1109/TCE.2024.3439719 10.1016/j.compbiomed.2023.107789 10.2214/AJR.20.23304 10.1136/thoraxjnl-2017-211280 10.1007/s00330-020-06918-2 10.1016/j.patrec.2020.09.010 10.1109/TBME.2021.3117407 10.1038/s41591-018-0300-7 10.1148/radiol.2020203511 10.1016/j.ejrad.2023.110858 10.1148/radiol.2020202944 10.1038/s41551-021-00704-1 10.1016/j.media.2021.102046 10.1016/j.health.2024.100332 10.1016/j.lanepe.2023.100664 10.3390/bioengineering11121290 10.1186/s13244-022-01250-3 10.1007/s11547-020-01200-3 10.1038/s41598-020-76550-z 10.1371/journal.pone.0252440 10.1038/s41598-023-44818-9 10.1002/qaj.481 10.1016/j.jcv.2020.104338 10.1007/s13755-021-00146-8 10.1016/j.clinimag.2020.04.001 10.1148/radiol.2462070712 10.1148/radiol.2020201365 10.1109/JBHI.2020.3037127 10.1016/j.acra.2023.03.006 10.1016/j.ejrad.2021.109548 10.1016/j.patcog.2020.107613 10.1109/ACCESS.2020.3010287 10.1109/TCE.2024.3411606 10.1148/radiol.213072 10.4103/jmp.jmp_100_21 10.1016/j.ijid.2023.10.001 10.1038/s41598-021-88538-4 10.1038/s41598-024-51317-y 10.1007/s00330-020-07018-x 10.1016/j.chaos.2024.115288 10.1007/s00530-023-01172-0 10.1148/radiol.2021204522 10.1609/aaai.v31i1.11231
ContentType	Journal Article
Copyright	Copyright: © 2025 Enshaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2025 Public Library of Science 2025 Enshaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 Enshaei et al 2025 Enshaei et al 2025 Enshaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: Copyright: © 2025 Enshaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. – notice: COPYRIGHT 2025 Public Library of Science – notice: 2025 Enshaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2025 Enshaei et al 2025 Enshaei et al – notice: 2025 Enshaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PTHSS PYCSY RC3 7X8 5PM DOA
DOI	10.1371/journal.pone.0328061
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Materials Science Collection ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection Biological Sciences Agriculture Science Database ProQuest Health & Medical Collection PML(ProQuest Medical Library) Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium ProQuest advanced technologies & aerospace journals ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition Engineering Collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	Agricultural Science Database CrossRef MEDLINE - Academic MEDLINE
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database (ProQuest) url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Sciences (General)
DocumentTitleAlternate	Differentiation of COVID-19 from other viral pneumonias and severity scoring on baseline chest radiographs
EISSN	1932-6203
ExternalDocumentID	3234616085 oai_doaj_org_article_b3742be7f8204ad6982e8a013f42d059 PMC12306774 A849697277 40729327 10_1371_journal_pone_0328061
Genre	Journal Article Comparative Study
GeographicLocations	Canada
GeographicLocations_xml	– name: Canada
GrantInformation_xml	– fundername: ;
GroupedDBID	--- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACCTH ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV AEAQA AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AHMBA ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAIFH BAWUL BBNVY BBTPI BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PV9 PYCSY RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM ADRAZ ALIPV CGR CUY CVF ECM EIF IPNFZ NPM RIG 3V. 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO ESTFP FR3 GNUQQ H94 K9. KL. M7N P64 PKEHL PQEST PQUKI RC3 7X8 PUEGO 5PM
ID	FETCH-LOGICAL-c644t-3123fe8142a0ac10d1efd753617aba0dc17ddb88a4fe92ca9b38c3e9820684f3
IEDL.DBID	DOA
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001539730400029&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1932-6203
IngestDate	Wed Oct 29 23:52:54 EDT 2025 Mon Nov 10 04:31:29 EST 2025 Tue Nov 04 02:04:45 EST 2025 Fri Sep 05 15:32:04 EDT 2025 Tue Oct 07 07:47:02 EDT 2025 Sat Nov 29 13:45:05 EST 2025 Sat Nov 29 10:28:44 EST 2025 Wed Nov 26 10:44:55 EST 2025 Wed Nov 26 10:44:48 EST 2025 Tue Aug 12 02:17:47 EDT 2025 Sat Aug 02 01:41:25 EDT 2025 Sat Nov 29 07:43:15 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	7
Language	English
License	Copyright: © 2025 Enshaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c644t-3123fe8142a0ac10d1efd753617aba0dc17ddb88a4fe92ca9b38c3e9820684f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ORCID	0000-0001-6996-237X 0000-0003-3696-2554
OpenAccessLink	https://doaj.org/article/b3742be7f8204ad6982e8a013f42d059
PMID	40729327
PQID	3234616085
PQPubID	1436336
PageCount	e0328061
ParticipantIDs	plos_journals_3234616085 doaj_primary_oai_doaj_org_article_b3742be7f8204ad6982e8a013f42d059 pubmedcentral_primary_oai_pubmedcentral_nih_gov_12306774 proquest_miscellaneous_3234700846 proquest_journals_3234616085 gale_infotracmisc_A849697277 gale_infotracacademiconefile_A849697277 gale_incontextgauss_ISR_A849697277 gale_incontextgauss_IOV_A849697277 gale_healthsolutions_A849697277 pubmed_primary_40729327 crossref_primary_10_1371_journal_pone_0328061
PublicationCentury	2000
PublicationDate	2025-07-29
PublicationDateYYYYMMDD	2025-07-29
PublicationDate_xml	– month: 07 year: 2025 text: 2025-07-29 day: 29
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: San Francisco – name: San Francisco, CA USA
PublicationTitle	PloS one
PublicationTitleAlternate	PLoS One
PublicationYear	2025
Publisher	Public Library of Science Public Library of Science (PLoS)
Publisher_xml	– name: Public Library of Science – name: Public Library of Science (PLoS)
References	A Signoroni (pone.0328061.ref014) 2021; 71 K Murphy (pone.0328061.ref059) 2020; 296 S Contreras (pone.0328061.ref002) 2023; 30 G Wang (pone.0328061.ref011) 2021; 5 S Tabik (pone.0328061.ref029) 2020; 24 L Lin (pone.0328061.ref056) 2021; 216 MEH Chowdhury (pone.0328061.ref052) 2020; 8 P Sahoo (pone.0328061.ref030) 2024; 238 F Chollet (pone.0328061.ref051) 2017 S Altmayer (pone.0328061.ref054) 2020; 30 S-J Yoo (pone.0328061.ref015) 2023; 164 DM Hansell (pone.0328061.ref040) 2008; 246 HYF Wong (pone.0328061.ref010) 2020; 296 AD Kaye (pone.0328061.ref001) 2021; 35 JE Lee (pone.0328061.ref007) 2022; 303 G Wu (pone.0328061.ref009) 2020; 30 RM Wehbe (pone.0328061.ref019) 2021; 299 MA Warren (pone.0328061.ref032) 2018; 73 M Hardy-Werbin (pone.0328061.ref018) 2023; 13 AM Hussein (pone.0328061.ref021) 2024; 14 T Chen (pone.0328061.ref033) 2024; 5 G Dasegowda (pone.0328061.ref064) 2023; 30 KL Gwet (pone.0328061.ref046) A Wong (pone.0328061.ref031) 2021; 11 A Miyazaki (pone.0328061.ref017) 2023; 13 A Jacobi (pone.0328061.ref008) 2020; 64 R Kozak (pone.0328061.ref037) 2020; 126 Z Wang (pone.0328061.ref061) 2021; 110 RR Selvaraju (pone.0328061.ref047) 2017 TR Nichols (pone.0328061.ref045) 2010; 13 C Szegedy (pone.0328061.ref050) 2016 A Sharafian (pone.0328061.ref024) 2024; 186 G Maguolo (pone.0328061.ref028) 2021; 76 EJ Topol (pone.0328061.ref063) 2019; 25 MA Talukder (pone.0328061.ref034) 2024; 168 P Afshar (pone.0328061.ref036) 2020; 138 JP Kanne (pone.0328061.ref012) 2021; 299 K Stefanidis (pone.0328061.ref039) 2021; 136 O Ronneberger (pone.0328061.ref041) 2015 A Degerli (pone.0328061.ref044) 2021; 9 GD Rubin (pone.0328061.ref053) 2020; 296 HX Bai (pone.0328061.ref005) 2020; 296 K Simonyan (pone.0328061.ref048) 2014 I Ullah (pone.0328061.ref025) 2024; 70 EJ Hwang (pone.0328061.ref020) 2021; 16 H Bilal (pone.0328061.ref026) 2024; 11 BP Kaur (pone.0328061.ref058) 2024; 14 EJ Chow (pone.0328061.ref003) 2023; 21 ML Schüz (pone.0328061.ref004) 2023; 137 S Manna (pone.0328061.ref006) 2020; 2 F Rizzetto (pone.0328061.ref055) 2022; 8 H Guan (pone.0328061.ref062) 2022; 69 R Zhang (pone.0328061.ref057) 2021; 298 N Ahmed (pone.0328061.ref027) 2023; 29 C Szegedy (pone.0328061.ref042) 2017; 31 DK Verma (pone.0328061.ref022) 2022; 47 J Sun (pone.0328061.ref016) 2022; 4 L Wang (pone.0328061.ref035) 2020; 10 A Ali (pone.0328061.ref023) 2024; 70 N Lee (pone.0328061.ref038) 2021; 193 A Borghesi (pone.0328061.ref013) 2020; 125 S Ruder (pone.0328061.ref043) 2017 K He (pone.0328061.ref049) 2016 A Albiol (pone.0328061.ref060) 2022; 13
References_xml	– volume: 238 start-page: 121724 year: 2024 ident: pone.0328061.ref030 article-title: A Multi-stage framework for COVID-19 detection and severity assessment from chest radiography images using advanced fuzzy ensemble technique publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.121724 – volume-title: Handbook of Inter-Rater Reliability Fifth Edition The Definitive Guide to Measuring the Extent of Agreement Among Raters Volume 1 Analysis of Categorical Ratings ident: pone.0328061.ref046 – volume: 4 issue: 4 year: 2022 ident: pone.0328061.ref016 article-title: Performance of a chest radiograph AI diagnostic tool for COVID-19: a prospective observational study publication-title: Radiol Artif Intell – volume: 14 start-page: 534 issue: 1 year: 2024 ident: pone.0328061.ref021 article-title: Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs publication-title: Sci Rep doi: 10.1038/s41598-023-47038-3 – volume: 296 issue: 3 year: 2020 ident: pone.0328061.ref059 article-title: COVID-19 on chest radiographs: a multireader evaluation of an artificial intelligence system publication-title: Radiology doi: 10.1148/radiol.2020201874 – start-page: 1251 volume-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) year: 2017 ident: pone.0328061.ref051 article-title: Xception: Deep Learning with Depthwise Separable Convolutions. – volume: 2 issue: 3 year: 2020 ident: pone.0328061.ref006 article-title: COVID-19: a multimodality review of radiologic techniques, clinical utility, and imaging features publication-title: Radiol Cardiothorac Imaging doi: 10.1148/ryct.2020200210 – volume: 13 start-page: 18761 issue: 1 year: 2023 ident: pone.0328061.ref018 article-title: MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis publication-title: Sci Rep doi: 10.1038/s41598-023-46126-8 – volume: 76 start-page: 1 year: 2021 ident: pone.0328061.ref028 article-title: A critic evaluation of methods for COVID-19 automatic detection from X-ray images publication-title: Inf Fusion doi: 10.1016/j.inffus.2021.04.008 – year: 2017 ident: pone.0328061.ref043 publication-title: An Overview of Multi-Task Learning in Deep Neural Networks – volume: 8 start-page: 2815 issue: 6 year: 2022 ident: pone.0328061.ref055 article-title: Diagnostic performance in differentiating COVID-19 from other viral pneumonias on CT imaging: multi-reader analysis compared with an artificial intelligence-based model publication-title: Tomography doi: 10.3390/tomography8060235 – volume: 35 start-page: 293 issue: 3 year: 2021 ident: pone.0328061.ref001 article-title: Economic impact of COVID-19 pandemic on healthcare facilities and systems: International perspectives publication-title: Best Pract Res Clin Anaesthesiol doi: 10.1016/j.bpa.2020.11.009 – volume: 70 start-page: 7252 issue: 4 year: 2024 ident: pone.0328061.ref023 article-title: A Resource-aware multi-graph neural network for urban traffic flow prediction in multi-access edge computing systems publication-title: IEEE Trans Consumer Electron doi: 10.1109/TCE.2024.3439719 – year: 2014 ident: pone.0328061.ref048 publication-title: Very Deep Convolutional Networks for Large-Scale Image Recognition – volume: 168 start-page: 107789 year: 2024 ident: pone.0328061.ref034 article-title: Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2023.107789 – volume: 216 start-page: 71 issue: 1 year: 2021 ident: pone.0328061.ref056 article-title: CT manifestations of coronavirus Disease (COVID-19) pneumonia and influenza virus pneumonia: a comparative study publication-title: AJR Am J Roentgenol doi: 10.2214/AJR.20.23304 – volume: 73 start-page: 840 issue: 9 year: 2018 ident: pone.0328061.ref032 article-title: Severity scoring of lung oedema on the chest radiograph is associated with clinical outcomes in ARDS publication-title: Thorax doi: 10.1136/thoraxjnl-2017-211280 – volume: 30 start-page: 5217 issue: 9 year: 2020 ident: pone.0328061.ref009 article-title: Mobile X-rays are highly valuable for critically ill COVID patients compliance with ethical standards publication-title: Eur Radiol doi: 10.1007/s00330-020-06918-2 – volume: 138 start-page: 1 year: 2020 ident: pone.0328061.ref036 article-title: COVID-CAPS: a capsule network-based framework for identification of COVID-19 cases from X-ray images publication-title: Pattern Recognit Lett doi: 10.1016/j.patrec.2020.09.010 – volume: 69 start-page: 1173 issue: 3 year: 2022 ident: pone.0328061.ref062 article-title: Domain adaptation for medical image analysis: a survey publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2021.3117407 – volume: 25 start-page: 44 issue: 1 year: 2019 ident: pone.0328061.ref063 article-title: High-performance medicine: the convergence of human and artificial intelligence publication-title: Nat Med doi: 10.1038/s41591-018-0300-7 – volume: 299 issue: 1 year: 2021 ident: pone.0328061.ref019 article-title: DeepCOVID-XR: an artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large U.S. clinical data set publication-title: Radiology doi: 10.1148/radiol.2020203511 – start-page: 234 year: 2015 ident: pone.0328061.ref041 article-title: U-net: Convolutional networks for biomedical image segmentation publication-title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag – volume: 164 start-page: 110858 year: 2023 ident: pone.0328061.ref015 article-title: Generative adversarial network for automatic quantification of coronavirus disease 2019 pneumonia on chest radiographs publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2023.110858 – volume: 298 issue: 2 year: 2021 ident: pone.0328061.ref057 article-title: Diagnosis of coronavirus disease 2019 pneumonia by using chest radiography: value of artificial intelligence publication-title: Radiology doi: 10.1148/radiol.2020202944 – volume: 5 start-page: 509 issue: 6 year: 2021 ident: pone.0328061.ref011 article-title: A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images publication-title: Nat Biomed Eng doi: 10.1038/s41551-021-00704-1 – volume: 71 start-page: 102046 year: 2021 ident: pone.0328061.ref014 article-title: BS-Net: learning COVID-19 pneumonia severity on a large chest X-ray dataset publication-title: Med Image Anal doi: 10.1016/j.media.2021.102046 – volume: 296 issue: 2 year: 2020 ident: pone.0328061.ref010 article-title: Frequency and distribution of chest radiographic findings in patients positive for COVID-19 publication-title: Radiology – start-page: 2818 volume-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) year: 2016 ident: pone.0328061.ref050 article-title: Rethinking the Inception Architecture for Computer Vision. – volume: 5 start-page: 100332 year: 2024 ident: pone.0328061.ref033 article-title: A vision transformer machine learning model for COVID-19 diagnosis using chest X-ray images publication-title: Healthc Anal doi: 10.1016/j.health.2024.100332 – volume: 30 start-page: 100664 year: 2023 ident: pone.0328061.ref002 article-title: From emergency response to long-term management: the many faces of the endemic state of COVID-19 publication-title: Lancet Reg Health Eur doi: 10.1016/j.lanepe.2023.100664 – volume: 11 start-page: 1290 issue: 12 year: 2024 ident: pone.0328061.ref026 article-title: An intelligent approach for early and accurate predication of cardiac disease using hybrid artificial intelligence techniques publication-title: Bioengineering (Basel) doi: 10.3390/bioengineering11121290 – volume: 13 start-page: 122 issue: 1 year: 2022 ident: pone.0328061.ref060 article-title: A comparison of Covid-19 early detection between convolutional neural networks and radiologists publication-title: Insights Imaging doi: 10.1186/s13244-022-01250-3 – volume: 125 start-page: 509 issue: 5 year: 2020 ident: pone.0328061.ref013 article-title: COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression publication-title: Radiol Med doi: 10.1007/s11547-020-01200-3 – volume: 10 start-page: 19549 issue: 1 year: 2020 ident: pone.0328061.ref035 article-title: COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images publication-title: Sci Rep doi: 10.1038/s41598-020-76550-z – volume: 16 issue: 6 year: 2021 ident: pone.0328061.ref020 article-title: COVID-19 pneumonia on chest X-rays: performance of a deep learning-based computer-aided detection system publication-title: PLoS One doi: 10.1371/journal.pone.0252440 – volume: 13 issue: 1 year: 2023 ident: pone.0328061.ref017 article-title: Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system publication-title: Sci Rep doi: 10.1038/s41598-023-44818-9 – volume: 13 start-page: 57 year: 2010 ident: pone.0328061.ref045 article-title: Putting the Kappa Statistic to Use publication-title: Qual Assur J doi: 10.1002/qaj.481 – volume: 126 start-page: 104338 year: 2020 ident: pone.0328061.ref037 article-title: Severity of coronavirus respiratory tract infections in adults admitted to acute care in Toronto, Ontario publication-title: J Clin Virol doi: 10.1016/j.jcv.2020.104338 – volume: 9 start-page: 15 issue: 1 year: 2021 ident: pone.0328061.ref044 article-title: COVID-19 infection map generation and detection from chest X-ray images publication-title: Health Inf Sci Syst doi: 10.1007/s13755-021-00146-8 – volume: 64 start-page: 35 year: 2020 ident: pone.0328061.ref008 article-title: Portable chest X-ray in coronavirus disease-19 (COVID-19): a pictorial review publication-title: Clin Imaging doi: 10.1016/j.clinimag.2020.04.001 – volume: 21 start-page: 195 issue: 3 year: 2023 ident: pone.0328061.ref003 article-title: The effects of the COVID-19 pandemic on community respiratory virus activity publication-title: Nat Rev Microbiol – volume: 246 start-page: 697 issue: 3 year: 2008 ident: pone.0328061.ref040 article-title: Fleischner Society: glossary of terms for thoracic imaging publication-title: Radiology doi: 10.1148/radiol.2462070712 – volume: 296 start-page: 172 issue: 1 year: 2020 ident: pone.0328061.ref053 article-title: The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the fleischner society publication-title: Radiology doi: 10.1148/radiol.2020201365 – volume: 193 issue: 13 year: 2021 ident: pone.0328061.ref038 article-title: Burden of noninfluenza respiratory viral infections in adults admitted to hospital: analysis of a multiyear Canadian surveillance cohort from 2 centres publication-title: CMAJ – volume: 24 start-page: 3595 issue: 12 year: 2020 ident: pone.0328061.ref029 article-title: COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest x-ray images publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2020.3037127 – volume: 30 start-page: 2921 issue: 12 year: 2023 ident: pone.0328061.ref064 article-title: Radiologist-trained AI model for identifying suboptimal chest-radiographs publication-title: Acad Radiol doi: 10.1016/j.acra.2023.03.006 – volume: 136 start-page: 109548 year: 2021 ident: pone.0328061.ref039 article-title: Radiological, epidemiological and clinical patterns of pulmonary viral infections publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2021.109548 – volume: 296 issue: 2 year: 2020 ident: pone.0328061.ref005 article-title: Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT publication-title: Radiology – start-page: 770 volume-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) year: 2016 ident: pone.0328061.ref049 article-title: Deep Residual Learning for Image Recognition – volume: 110 start-page: 107613 year: 2021 ident: pone.0328061.ref061 article-title: Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays publication-title: Pattern Recogn doi: 10.1016/j.patcog.2020.107613 – start-page: 618 volume-title: Proceedings of the IEEE International Conference on Computer Vision (ICCV) year: 2017 ident: pone.0328061.ref047 article-title: Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization – volume: 8 start-page: 132665 year: 2020 ident: pone.0328061.ref052 article-title: Can AI help in screening viral and COVID-19 pneumonia? publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3010287 – volume: 70 start-page: 6871 issue: 4 year: 2024 ident: pone.0328061.ref025 article-title: Revolutionizing e-commerce with consumer-driven energy-efficient WSNs: a multi-characteristics approach publication-title: IEEE Trans Consumer Electron doi: 10.1109/TCE.2024.3411606 – volume: 303 start-page: 682 issue: 3 year: 2022 ident: pone.0328061.ref007 article-title: Imaging and clinical features of COVID-19 breakthrough infections: a multicenter study publication-title: Radiology doi: 10.1148/radiol.213072 – volume: 47 start-page: 57 issue: 1 year: 2022 ident: pone.0328061.ref022 article-title: Classifying COVID-19 and viral pneumonia lung infections through deep convolutional neural network model using chest X-Ray Images publication-title: J Med Phys doi: 10.4103/jmp.jmp_100_21 – volume: 137 start-page: 16 year: 2023 ident: pone.0328061.ref004 article-title: Global prevalence of respiratory virus infections in adults and adolescents during the COVID-19 pandemic: a systematic review and meta-analysis publication-title: Int J Infect Dis doi: 10.1016/j.ijid.2023.10.001 – volume: 11 start-page: 9315 issue: 1 year: 2021 ident: pone.0328061.ref031 article-title: Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays publication-title: Sci Rep doi: 10.1038/s41598-021-88538-4 – volume: 14 start-page: 1136 issue: 1 year: 2024 ident: pone.0328061.ref058 article-title: An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time publication-title: Sci Rep doi: 10.1038/s41598-024-51317-y – volume: 30 start-page: 6485 issue: 12 year: 2020 ident: pone.0328061.ref054 article-title: Comparison of the computed tomography findings in COVID-19 and other viral pneumonia in immunocompetent adults: a systematic review and meta-analysis publication-title: Eur Radiol doi: 10.1007/s00330-020-07018-x – volume: 186 start-page: 115288 year: 2024 ident: pone.0328061.ref024 article-title: Adaptive fuzzy backstepping secure control for incommensurate fractional order cyber–physical power systems under intermittent denial of service attacks publication-title: Chaos, Solitons Fractals doi: 10.1016/j.chaos.2024.115288 – volume: 29 start-page: 3877 issue: 6 year: 2023 ident: pone.0328061.ref027 article-title: Images denoising for COVID-19 chest X-ray based on multi-scale parallel convolutional neural network publication-title: Multimedia Syst doi: 10.1007/s00530-023-01172-0 – volume: 299 issue: 3 year: 2021 ident: pone.0328061.ref012 article-title: COVID-19 imaging: what we know now and what remains unknown publication-title: Radiology doi: 10.1148/radiol.2021204522 – volume: 31 issue: 1 year: 2017 ident: pone.0328061.ref042 article-title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning publication-title: Proceedings of the AAAI Conference on Artificial Intelligence doi: 10.1609/aaai.v31i1.11231
SSID	ssj0053866
Score	2.4894345
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...
SourceID	plos doaj pubmedcentral proquest gale pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database
StartPage	e0328061
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
SummonAdditionalLinks	– databaseName: Nursing & Allied Health Database dbid: 7RV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZg4cAFKK8uFDAICTikXT9IYi6obKnopUWlWvUWOX4sK1VJ2Ozyq_iRzDjelKAKIaG9rSeJY3-ehzP-hpBXpRIafiwpc2cS6VOXgJXTybvSMp-W0hktQ7GJ7Pg4Pz9XX-KGWxvTKjc6MShqWxvcI98TXMiUpeAhfGi-J1g1Cr-uxhIa18kNhr4x4Dk7nW00MazlNI3H5UTG9uLs7DZ15XaRR26SsoE5Cqz9vW4eNRd1e5Xj-Wf-5G8G6fDO_77KXXI7uqJ0v8POFrnmqntkKy72lr6JjNRv75OfB7GMyqqbSFp7Oj2ZHR0kTFE8oELDOS6K-7ktNmLm8AVtKreGZy001ZWlYIIdVsqjrQlJfxTugzYU_VwaynbRpbaLjkK7fU-nfYVEvKN1rqGxxMWc4u4xDbmQyTLkYtNL1vIH5Ozw09n0cxLLPCQGnLEVWAEuvMuZ5HqiDZtY5ryFKAp8K13qiTUss7bMcy29U9xoVYrcCKeQeT6XXjwkowpmdJtQz4U2ghvlJJclxHKWKzTDzqWCAwzHJNlMdtF0ZB5F-KKXQRDUDXqB4CgiOMbkIyKil0Uq7vBHvZwXcWUXpcgkL13moT9S2xT65XINnrWX3ILzOibPEU9Fd661VyjFfi5VqsB9zMbkZZBAOo4K833met22xdHJ7B-Evp4OhF5HIV8DMo2OZyzgnZDmayC5M5AEpWIGzduI_s2otMUlZuHKDaqvbn7RN-NNMYevcvW6k8mwfkM6Jo-6BdSPLNL0QSQBz80HS2sw9MOWavEtsKEzDKIhiHn89349Ibc4lm6eZAlXO2S0Wq7dU3LT_Fgt2uWzoDd-ATvee38 priority: 102 providerName: ProQuest – databaseName: Public Library of Science (PLoS) Journals Open Access dbid: FPL link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELbQwoELtLy6tIBBSMAh7cY2TsytbFlRCbUVVFVvkeNHWalKos0uv4ofyYzjTUnVSqDc1hPHO55nMv6GkLel4hquNClzZxLhpUvAy-nkY2lTL0vhjBah2UR2dJSfn6uTq0Tx2hd8nqV7kae7TV25XUR_m2C2c5dxKTHZmp18W1te0F0p4_G42-4cuJ-A0t_b4lFzWbc3BZrX6yX_ckCzh_-79A3yIIaadL-TjU1yx1WPyGZU5pa-j4jTHx6T3wexTcqy2yhaezo9Pjs8SFJF8QAKDee0KL6vbXEQK4MvaVO5FUjxXFNdWQou1mEnPNqaUNRHYR70kRjH0tCWiy60nXcQ2e0nOu07IOKM1rmGxhYWFxTfDtNQ65gsQq01vUIlf0JOZ19Op1-T2MYhMRBsLcHKM-5dngqmJ9qkE5s6byFLgthJl3piTZpZW-a5Ft4pZrQqeW64U4gsnwvPn5JRBQzcItQzrg1nRjnBRAm5mmUK3axzkjMQszFJ1ptbNB1YRxG-2GWQ5HRML3AvirgXY_IZJaCnRajt8ANsYhE1tyh5JljpMg_rEdpKWJfLNUTOXjALwemYvEL5Kbpzq73BKPZzoaSC8DAbkzeBAuE2KqznudCrti0Oj8_-gejH9wHRu0jka5BEo-MZCvhPCOM1oNwZUILRMIPhLZT2NVfagjMuZCohAIc71xpw8_DrfhgnxRq9ytWrjibD_gxyTJ51CtNzFmH4IFOA5-YDVRqwfjhSzX8GtPMUk2RIUp7fvuRtcp9hW-ZJljC1Q0bLxcq9IPfMr-W8XbwMNuIPtDhprg priority: 102 providerName: Public Library of Science
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
URI	https://www.ncbi.nlm.nih.gov/pubmed/40729327 https://www.proquest.com/docview/3234616085 https://www.proquest.com/docview/3234700846 https://pubmed.ncbi.nlm.nih.gov/PMC12306774 https://doaj.org/article/b3742be7f8204ad6982e8a013f42d059 http://dx.doi.org/10.1371/journal.pone.0328061
Volume	20
WOSCitedRecordID	wos001539730400029&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: DOA dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Agricultural Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M0K dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7S dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PATMY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7X7 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database (Proquest) customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: KB. dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/materialsscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7RV dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: P5Z dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Biological Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7P dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: BENPR dateStart: 20061201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 8C1 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database (ProQuest) customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PIMPY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVATS databaseName: Public Library of Science (PLoS) Journals Open Access customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: FPL dateStart: 20060101 isFulltext: true titleUrlDefault: http://www.plos.org/publications/ providerName: Public Library of Science
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3db9MwELeg8MALYnytMIpBSMBDusTxEpu3tVtFNdZF3VQNXiLHdkalKa2alr-KP5I7Jw0LmgQPqNI91Be38Z3vIzn_jpB3mQwVfAIvE1Z7PI-sB15OeQeZCfIo41Yr7ppNxJOJuLyUyY1WX1gTVsEDVwu3n4WQvGU2zsFVcWUiKZgVCgKXnDMDsQFaXz-W22SqssGwi6OoPigXxsF-LZf-clHYPiLI-VHQckQOr7-xyp3l9aK8LeT8s3LyhisaPSIP6xiSHlb_fYfcscVjslPv0pJ-qKGkPz4hP4_q_ifrSgJ0kdPh2Wx85AWS4skS6g5gUXwQW-Iglvxe02VhN6Cec0VVYSj4Tost7mipXbUehXnQ-WGASl2_LbpSZl5hX5ef6LBpbYgzGmuXtO5NcUXxsS91RYzeyhVR099w40_Jxej4YvjZq_szeBqiqDWYbxbmVgScKV_pwDeBzQ2kPxAUqUz5RgexMZkQiudWMq1kFgodWomQ8YLn4TPSKUAgu4TmLFQ6ZFpazngGSZhhEv2ntVHIQH-6xNvKKl1WKBypexUXQ_ZSLXqKsk1r2XbJAAXa8CKGtvsCNCutNSv9m2Z1yWtUh7Q6kNpYgvRQcBlJiPviLnnrOBBHo8BCnSu1Kct0fDb7B6bzaYvpfc2UL0CxtKoPR8A9IT5Xi3OvxQnWQLeGd1F5t6tSpiELeRREEFnDlVuFvn34TTOMk2LxXWEXm4onxsYLUZc8r_S_WVnE14MUAH5XtHZGa-nbI8X8u4MxDzD7hezjxf8Q1kvygGFnZj_2mNwjnfVqY1-R-_rHel6ueuRuPJ0hvYwdFUDFMOiRe4PjSTLtOeMBdJR8AXoy6AM99U-Qxomj50CTg29wRTI-Tb7-AqCQeU8
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3db9MwELdGQYIXYHytMJhBIOAhW-J4-UBCaLRMqza6Capp4iVybKdUmpLQtCD-J_gfuXOcjKAJ8bIH1Lf66iaX-3Tu7kfI0zT2BXw8J420dHgWaAe8nHC2U-VlQcq1FNyATYTjcXRyEh-tkJ9NLwyWVTY20RhqVUg8I9_ymc8DL4AI4U35xUHUKHy72kBo1GKxr79_g5Stej0awvN9xtjuu8lgz7GoAo4E378Ao8P8TEceZ8IV0nOVpzMFQTu4cpEKV0kvVCqNIsEzHTMp4tSPpK9jHHQe8cyHbS-Ry5wzF5XoaPtTY_jBdASB7c7zQ2_LCsNmWeR6E8fWuYHX8X4GJKB1Bb3ytKjOi3P_LNf8zf_t3vjPOHeTXLeBNt2pNWOVrOj8Flm1pqyiL-y87Ze3yY-hBYlZ1GJKi4wODo9HQ8eLKbbfUNOlRvG0usJFrIs-pWWul3BrM0FFrigEGBpxAGklTUkjhX0wQsAonhpQMjoXalYPCK9e0UGL_4g7Kq1LagE8phTPxqmp9HTmptKcns1kv0MmF8G0u6SXgwCtEZoxX0ifyVhzxlPIVBWLMcjQOvAZKFmfOI1sJWU9qiQx7ytDSPFqpicoi4mVxT55iwLY0uKgcfNFMZ8m1m4lqR9yluowg-vhQgVwXToSkDdknCkIzftkA8U3qbt2W3OZ7EQ8DmIIjsM-eWIocNhIjtVMU7GsqmR0ePwPRB8_dIieW6KsAEWQwnaQwD3hELMO5XqHEkym7CyvobI1XKmSMxWBXzZKdP7y43YZN8UKxVwXy5omRHSKoE_u1frachaHEEKeBP8bdTS5w_ruSj77bGa9e3hEACna_b9f1wa5ujd5f5AcjMb7D8g1hiDVbuiweJ30FvOlfkiuyK-LWTV_ZEwWJckFK_ov39TYbw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VgBAXoLwaKHRBIODgJl5v_UBCqCREREVpRauq4mKt9xEiVbaJExD_iT_Av2NmvU4xqhCXHlBu2cnGHs9zPTMfIU-zJBDw8b0s1tLjJtQeeDnh7WTKN2HGtRTcgk1Ek0l8cpIcrJGfTS8MllU2NtEaalVIPCPvBSzgoR9ChNAzriziYDh6U37xEEEK37Q2cBq1iOzp798gfatej4fwrJ8xNnp3NHjvOYQBT0IcsAADxAKjY58z0RfS7ytfGwUBPLh1kYm-kn6kVBbHghudMCmSLIhloBMceh5zE8C2l8jlCFJMrCY82PnUOAEwI2HoOvWCyO85wdgui1xv4wi7fui3PKEFDFi5hU55WlTnxbx_lm7-5gtHN_5jLt4k110ATndrjVknazq_RdadiavoCzeH--Vt8mPowGMWtfjSwtDB_vF46PkJxbYcarvXKJ5iV7iI9dKntMz1Em5tJqjIFYXAQyM-IK2kLXWksA9GDhjdUwtWRudCzerB4dUrOljhQuKOSuuSOmCPKcUzc2orQL25rUCnZ7Pa75Cji2DaXdLJQZg2CDUsEDJgMtGc8QwyWMUSDD60DgMGytclXiNnaVmPMEnte8wIUr-a6SnKZerkskveojCuaHEAuf2imE9TZ8_SLIg4y3Rk4Hq4UCFcl44F5BOGMwUhe5dsoSindTfvyoymuzFPwgSC5qhLnlgKHEKSoyBOxbKq0vH-8T8QHX5sET13RKYApZDCdZbAPeFwsxblZosSTKlsLW-g4jVcqdIzdYFfNgp1_vLj1TJuipWLuS6WNU2EqBVhl9yrdXfFWRxOCPkT_G_c0uoW69sr-eyznQHv49EBpG73_35dW-Qq6Hf6YTzZe0CuMcSu7kceSzZJZzFf6ofkivy6mFXzR9Z6UZJesJ7_Akba4Tk
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Differentiation+of+COVID-19+from+other+types+of+viral+pneumonia+and+severity+scoring+on+baseline+chest+radiographs%3A+Comparison+of+deep+learning+with+multi-reader+evaluation&rft.jtitle=PloS+one&rft.au=Enshaei%2C+Nastaran&rft.au=Mohammadi%2C+Arash&rft.au=Naderkhani%2C+Farnoosh&rft.au=Daneman%2C+Nick&rft.date=2025-07-29&rft.pub=Public+Library+of+Science&rft.issn=1932-6203&rft.eissn=1932-6203&rft.volume=20&rft.issue=7&rft.spage=e0328061&rft_id=info:doi/10.1371%2Fjournal.pone.0328061&rft.externalDocID=A849697277
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon