期刊
NATURE COMMUNICATIONS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-17971-2
关键词
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资金
- NIH Center for Interventional Oncology
- Intramural Research Program of the National Institutes of Health (NIH) by intramural NIH [NIH Z01 1ZID, BC011242, CL040015]
- NIH Intramural Targeted Anti-COVID-19 (ITAC) Program
- National Cancer Institute, National Institutes of Health [75N91019D00024, 75N91019F00129]
- French Society of Radiology
- French Academic College of Radiology
- NATIONAL CANCER INSTITUTE [ZIABC010654] Funding Source: NIH RePORTER
Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.
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