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Open Access

Peer-reviewed

Research Article

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs

Jocelyn Zhu,

Roles Conceptualization, Data curation, Formal analysis, Methodology, Validation, Writing – review & editing

Affiliation Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America
⨯
Beiyi Shen,

Roles Data curation, Formal analysis, Writing – review & editing

Affiliation Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America
⨯
Almas Abbasi,

Roles Data curation, Formal analysis

Affiliation Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America
⨯
Mahsa Hoshmand-Kochi,

Roles Data curation, Formal analysis

Affiliation Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America
⨯
Haifang Li,

Roles Data curation, Formal analysis, Investigation, Writing – review & editing

Affiliation Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America
⨯
Tim Q. Duong

Roles Conceptualization, Investigation, Supervision, Writing – review & editing

* E-mail: tim.duong@stonybrook.edu

Affiliation Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America

http://orcid.org/0000-0001-6403-2827

⨯

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs

Jocelyn Zhu,
Beiyi Shen,
Almas Abbasi,
Mahsa Hoshmand-Kochi,
Haifang Li,
Tim Q. Duong

x

Published: July 28, 2020
https://doi.org/10.1371/journal.pone.0236621

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COVID 19
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Opacity
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Radiologists
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Artificial intelligence
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Radiology and imaging
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Respiratory infections
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Machine learning algorithms
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