References
- 1Ye Z, Zhang Y, Wang Y, Huang Z, Song B. Chest CT manifestations of new coronavirus disease 2019 (COVID-19): A pictorial review. Eur Radiol. 2020; 30: 4381–4389. DOI: 10.1007/s00330-020-06801-0
- 2Li K, Fang Y, Li W, et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol. 2020; 30: 4407–4416. DOI: 10.1007/s00330-020-06817-6
- 3Zhou S, Zhu T, Wang Y, Xia L. Imaging features and evolution on CT in 100 COVID-19 pneumonia patients in Wuhan, China. Eur Radiol. 2020; 30: 5446–5454. DOI: 10.1007/s00330-020-06879-6
- 4Lessmann N, Sánchez CI, Beenen L, et al. Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence. Radiology. 2020; 298(1): e18–e28. DOI: 10.1148/radiol.2020202439
- 5Huang L, Han R, Ai T, et al. Serial quantitative chest CT assessment of COVID-19: Deep-learning approach. Radiology: Cardiothoracic Imaging. 2020; 2(2):
e200075 . DOI: 10.1148/ryct.2020200075 - 6Li L, Qin L, Xu Z, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy. Radiology. 2020; 296(2): e65–e71. DOI: 10.1148/radiol.2020200905
- 7Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neuronal networks. Comput Biol Med. 2020; 121: 103795. DOI: 10.1016/j.compbiomed.2020.103795
- 8Wang S, Zha Y, Li W, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J. 2020; 56(2): 2000775. DOI: 10.1183/13993003.00775-2020
- 9Mei X, Lee HC, Diao KY, et al. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat Med. 2020; 26: 1224–1228. DOI: 10.1038/s41591-020-0931-3
- 10Rubin G, Ryerson C, Haramati L, et al. The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the Fleishner Society. Radiology. 2020; 296(1): 172–180. DOI: 10.1148/radiol.2020201365
- 11Chaganti S, Grenier P, Balachandran A, et al. Automated wuantification of CT patterns associated with COVID-19 from chest CT. Radiology: Artificial Intelligence. 2020; 2(4). DOI: 10.1148/ryai.2020200048
- 12Bernheim A, Mei X, Huang M, et al. Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection. Radiology. 2020; 295(3): 200463. DOI: 10.1148/radiol.2020200463
- 13Laghi A. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence. Lancet Digit Health. 2020; 2(5):
e225 . DOI: 10.1016/S2589-7500(20)30079-0 - 14Waller JV, Kaur P, Tucker A, et al. Diagnostic tools for coronavirus disease (COVID-19): Comparing CT and RT-PCR viral nucleic acid testing. AJR Am J Roentgenol. 2020; 215(4): 834–838. DOI: 10.2214/AJR.20.23418