An artificial intelligence deep learning platform achieves high diagnostic accuracy for Covid-19 pneumonia by reading chest X-ray images
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UMass Chan Affiliations
Division of Hematology Oncology, Department of MedicineDocument Type
Journal ArticlePublication Date
2022-04-15Keywords
Artificial intelligenceRadiology
Virology
Artificial Intelligence and Robotics
Diagnosis
Health Information Technology
Infectious Disease
Radiology
Virus Diseases
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Show full item recordAbstract
The coronavirus disease of 2019 (Covid-19) causes deadly lung infections (pneumonia). Accurate clinical diagnosis of Covid-19 is essential for guiding treatment. Covid-19 RNA test does not reflect clinical features and severity of the disease. Pneumonia in Covid-19 patients could be caused by non-Covid-19 organisms and distinguishing Covid-19 pneumonia from non-Covid-19 pneumonia is critical. Chest X-ray detects pneumonia, but a high diagnostic accuracy is difficult to achieve. We develop an artificial intelligence-based (AI) deep learning method with a high diagnostic accuracy for Covid-19 pneumonia. We analyzed 10,182 chest X-ray images of healthy individuals, bacterial pneumonia. and viral pneumonia (Covid-19 and non-Covid-19) to build and test AI models. Among viral pneumonia, diagnostic accuracy for Covid-19 reaches 99.95%. High diagnostic accuracy is also achieved for distinguishing Covid-19 pneumonia from bacterial pneumonia (99.85% accuracy) or normal lung images (100% accuracy). Our AI models are accurate for clinical diagnosis of Covid-19 pneumonia by reading solely chest X-ray images.Source
Li D, Li S. An artificial intelligence deep learning platform achieves high diagnostic accuracy for Covid-19 pneumonia by reading chest X-ray images. iScience. 2022 Apr 15;25(4):104031. doi: 10.1016/j.isci.2022.104031. Epub 2022 Mar 6. PMID: 35280932; PMCID: PMC8898091. Link to article on publisher's site
DOI
10.1016/j.isci.2022.104031Permanent Link to this Item
http://hdl.handle.net/20.500.14038/27574PubMed ID
35280932Related Resources
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Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Distribution License
http://creativecommons.org/licenses/by-nc-nd/4.0/ae974a485f413a2113503eed53cd6c53
10.1016/j.isci.2022.104031
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Except where otherwise noted, this item's license is described as Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).