Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets
Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls.
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-17971-2 |
| _version_ | 1818856006799589376 |
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| author | Stephanie A. Harmon Thomas H. Sanford Sheng Xu Evrim B. Turkbey Holger Roth Ziyue Xu Dong Yang Andriy Myronenko Victoria Anderson Amel Amalou Maxime Blain Michael Kassin Dilara Long Nicole Varble Stephanie M. Walker Ulas Bagci Anna Maria Ierardi Elvira Stellato Guido Giovanni Plensich Giuseppe Franceschelli Cristiano Girlando Giovanni Irmici Dominic Labella Dima Hammoud Ashkan Malayeri Elizabeth Jones Ronald M. Summers Peter L. Choyke Daguang Xu Mona Flores Kaku Tamura Hirofumi Obinata Hitoshi Mori Francesca Patella Maurizio Cariati Gianpaolo Carrafiello Peng An Bradford J. Wood Baris Turkbey |
| author_facet | Stephanie A. Harmon Thomas H. Sanford Sheng Xu Evrim B. Turkbey Holger Roth Ziyue Xu Dong Yang Andriy Myronenko Victoria Anderson Amel Amalou Maxime Blain Michael Kassin Dilara Long Nicole Varble Stephanie M. Walker Ulas Bagci Anna Maria Ierardi Elvira Stellato Guido Giovanni Plensich Giuseppe Franceschelli Cristiano Girlando Giovanni Irmici Dominic Labella Dima Hammoud Ashkan Malayeri Elizabeth Jones Ronald M. Summers Peter L. Choyke Daguang Xu Mona Flores Kaku Tamura Hirofumi Obinata Hitoshi Mori Francesca Patella Maurizio Cariati Gianpaolo Carrafiello Peng An Bradford J. Wood Baris Turkbey |
| author_sort | Stephanie A. Harmon |
| collection | DOAJ |
| description | Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls. |
| first_indexed | 2024-12-19T08:17:38Z |
| format | Article |
| id | doaj.art-6f9c98cf760e4d96a63cb5040206bc04 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-19T08:17:38Z |
| publishDate | 2020-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-6f9c98cf760e4d96a63cb5040206bc042022-12-21T20:29:27ZengNature PortfolioNature Communications2041-17232020-08-011111710.1038/s41467-020-17971-2Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasetsStephanie A. Harmon0Thomas H. Sanford1Sheng Xu2Evrim B. Turkbey3Holger Roth4Ziyue Xu5Dong Yang6Andriy Myronenko7Victoria Anderson8Amel Amalou9Maxime Blain10Michael Kassin11Dilara Long12Nicole Varble13Stephanie M. Walker14Ulas Bagci15Anna Maria Ierardi16Elvira Stellato17Guido Giovanni Plensich18Giuseppe Franceschelli19Cristiano Girlando20Giovanni Irmici21Dominic Labella22Dima Hammoud23Ashkan Malayeri24Elizabeth Jones25Ronald M. Summers26Peter L. Choyke27Daguang Xu28Mona Flores29Kaku Tamura30Hirofumi Obinata31Hitoshi Mori32Francesca Patella33Maurizio Cariati34Gianpaolo Carrafiello35Peng An36Bradford J. Wood37Baris Turkbey38Molecular Imaging Branch, National Cancer Institute, National Institutes of HealthState University of New York-Upstate Medical CenterCenter for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of HealthRadiology and Imaging Sciences, Clinical Center, National Institutes of HealthNVIDIA CorporationNVIDIA CorporationNVIDIA CorporationNVIDIA CorporationCenter for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of HealthCenter for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of HealthCenter for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of HealthCenter for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of HealthCenter for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of HealthCenter for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of HealthMolecular Imaging Branch, National Cancer Institute, National Institutes of HealthCenter for Research in Computer Vision, University of Central FloridaDepartment of Radiology Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico MilanoDepartment of Radiology Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico MilanoDepartment of Radiology Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico MilanoDiagnostic and Interventional Radiology Service, ASST Santi Paolo e Carlo, San Paolo HospitalPostgraduation School in Radiodiagnostics, Università Degli Studi di MilanoPostgraduation School in Radiodiagnostics, Università Degli Studi di MilanoState University of New York-Upstate Medical CenterRadiology and Imaging Sciences, Clinical Center, National Institutes of HealthRadiology and Imaging Sciences, Clinical Center, National Institutes of HealthRadiology and Imaging Sciences, Clinical Center, National Institutes of HealthRadiology and Imaging Sciences, Clinical Center, National Institutes of HealthMolecular Imaging Branch, National Cancer Institute, National Institutes of HealthNVIDIA CorporationNVIDIA CorporationSelf-Defense Forces Central HospitalSelf-Defense Forces Central HospitalSelf-Defense Forces Central HospitalDiagnostic and Interventional Radiology Service, ASST Santi Paolo e Carlo, San Paolo HospitalDiagnostic and Interventional Radiology Service, ASST Santi Paolo e Carlo, San Paolo HospitalDepartment of Radiology Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico MilanoDepartment of Radiology, Xiangyang NO.1 People’s Hospital Affiliated to Hubei University of Medicine XiangyangCenter for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of HealthMolecular Imaging Branch, National Cancer Institute, National Institutes of HealthChest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls.https://doi.org/10.1038/s41467-020-17971-2 |
| spellingShingle | Stephanie A. Harmon Thomas H. Sanford Sheng Xu Evrim B. Turkbey Holger Roth Ziyue Xu Dong Yang Andriy Myronenko Victoria Anderson Amel Amalou Maxime Blain Michael Kassin Dilara Long Nicole Varble Stephanie M. Walker Ulas Bagci Anna Maria Ierardi Elvira Stellato Guido Giovanni Plensich Giuseppe Franceschelli Cristiano Girlando Giovanni Irmici Dominic Labella Dima Hammoud Ashkan Malayeri Elizabeth Jones Ronald M. Summers Peter L. Choyke Daguang Xu Mona Flores Kaku Tamura Hirofumi Obinata Hitoshi Mori Francesca Patella Maurizio Cariati Gianpaolo Carrafiello Peng An Bradford J. Wood Baris Turkbey Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets Nature Communications |
| title | Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets |
| title_full | Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets |
| title_fullStr | Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets |
| title_full_unstemmed | Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets |
| title_short | Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets |
| title_sort | artificial intelligence for the detection of covid 19 pneumonia on chest ct using multinational datasets |
| url | https://doi.org/10.1038/s41467-020-17971-2 |
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