An artificial intelligence deep learning platform achieves high diagnostic accuracy for Covid-19 pneumonia by reading chest X-ray images
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-Co...
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Veröffentlicht in: | iScience 2022-04, Vol.25 (4), p.104031-104031, Article 104031 |
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Sprache: | eng |
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Zusammenfassung: | 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.
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•We used artificial intelligence models to diagnose Covid-19 pneumonia by reading chest X-ray images•We employed our unique deep learning voting algorithms combining multiple Convolutional neural networks•Our AI models reached a high diagnostic accuracy (>99%) for Covid-19 pneumonia detection•We obtained and analyzed a large chest X-ray image dataset (10,182 images)
Radiology; Virology; Artificial intelligence |
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ISSN: | 2589-0042 2589-0042 |
DOI: | 10.1016/j.isci.2022.104031 |