UMMS Affiliation

Division of Hematology Oncology, Department of Medicine

Publication Date

2022-04-15

Document Type

Article

Disciplines

Artificial Intelligence and Robotics | Diagnosis | Health Information Technology | Infectious Disease | Radiology | Virus Diseases

Abstract

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.

Keywords

Artificial intelligence, Radiology, Virology

Rights and Permissions

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/).

DOI of Published Version

10.1016/j.isci.2022.104031

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

Journal/Book/Conference Title

iScience

Related Resources

Link to Article in PubMed

PubMed ID

35280932

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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