| [1] |
Zheng J. SARS-CoV-2: an Emerging Coronavirus that Causes a Global Threat. Int J Biol Sci, 2020,16(10):1678-1685. doi: 10.7150/ijbs.45053. eCollection 2020.
URL
pmid: 32226285
|
| [2] |
WHO Coronavirus Disease (COVID-19) Dashboard(EB/OL). [2020-06-10]. https://covid19.who.int/.
|
| [3] |
Ai T, Yang Z, Hou H, et al. Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases[J/OL]. Radiology, 2020:200642. [2020-06-26]. doi: 10.1148/radiol.2020200642. Online ahead of print.
|
| [4] |
Fang Y, Zhang H, Xie J, et al. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR[J/OL]. Radiology, 2020:200432. [2020-06-26]. doi: 10.1148/radiol.2020200432. Online ahead of print.
|
| [5] |
Bernheim A, Mei X, Huang M, et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection[J/OL]. Radiology, 2020,295(3):200463. [2020-06-26]. doi: 10.1148/radiol.2020200463.Online ahead of print.
|
| [6] |
Kim H, Hong H, Yoon SH. Diagnostic Performance of CT and Reverse Transcriptase-Polymerase Chain Reaction for Coronavirus Disease 2019:A Meta-Analysis[J/OL]. Radiology, 2020:201343. [2020-06-26]. doi: 10.1148/radiol.2020201343. Online ahead of print.
|
| [7] |
Li K, Wu J, Wu F, et al. The Clinical and Chest CT Features Associated with Severe and Critical COVID-19 Pneumonia. Invest Radiol, 2020,55(6):327-331. doi: 10.1097/RLI.0000000000000672.
doi: 10.1097/RLI.0000000000000672
URL
pmid: 32118615
|
| [8] |
Dai WC, Zhang HW, Yu J, et al. CT Imaging and Differential Diagnosis of COVID-19. Can Assoc Radiol J, 2020,71(2):195-200. doi: 10.1177/0846537120913033.
URL
pmid: 32129670
|
| [9] |
Pan F, Ye T, Sun P, et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology, 2020,295(3):715-721. doi: 10.1148/radiol.2020200370.
URL
pmid: 32053470
|
| [10] |
Colombi D, Bodini FC, Petrini M, et al. Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia[J/OL]. Radiology, 2020:201433. [2020-06-26]. doi: 10.1148/radiol.2020201433. Online ahead of print.
|
| [11] |
Li K, Fang Y, Li W, et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)[J/OL]. Eur Radiol, 2020:1-10. [2020-06-26].doi: 10.1007/s00330-020-06817-6. Online ahead of print.
|
| [12] |
Lyu P, Liu X, Zhang R, et al. The performance of chest CT in evaluating the clinical severity of COVID-19 pneumonia: identifying critical cases based on CT characteristics[J/OL]. Invest Radiol , 2020:10.1097/RLI.0000000000000689. [2020-06-26].doi: 10.1097/RLI.0000000000000689. Online ahead of print.
|
| [13] |
Ding X, Xu J, Zhou J, et al. Chest CT findings of COVID-19 pneumonia by duration of symptoms. Eur J Radiol, 2020,127:109009. doi: 10.1016/j.ejrad.2020.109009.
doi: 10.1016/j.ejrad.2020.109009
URL
pmid: 32325282
|
| [14] |
Shen C, Yu N, Cai S, et al. Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019[J/OL]. J Pharm Anal, 2020. [2020-06-26]. doi: 10.1016/j.jpha.2020.03.004. Online ahead of print.
|
| [15] |
Cheng Z, Qin L, Cao Q, et al. Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia[J/OL]. Radiol Infect Dis, 2020. [2020-06-26]. doi: 10.1016/j.jrid.2020.04.004. Online ahead of print.
|
| [16] |
Qi X, Lei J, Yu Q, et al. CT imaging of coronavirus disease 2019 (COVID-19): from the qualitative to quantitative. Ann Transl Med, 2020,8(5):256. doi: 10.21037/atm.2020.02.91.
URL
pmid: 32309403
|
| [17] |
Yu Q, Wang Y, Huang S, et al. Multicenter cohort study demonstrates more consolidation in upper lungs on initial CT increases the risk of adverse clinical outcome in COVID-19 patients. Theranostics, 2020,10(12):5641-5648. doi: 10.7150/thno.46465.
URL
pmid: 32373237
|
| [18] |
Liu F, Zhang Q, Huang C, et al. CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients. Theranostics, 2020,10(12):5613-5622. doi: 10.7150/thno.45985.
doi: 10.7150/thno.45985
URL
pmid: 32373235
|
| [19] |
Li L, Qin L, Xu Z, et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT[J/OL]. Radiology, 2020:200905. [2020-06-26]. doi: 10.1148/radiol.2020200905. Online ahead of print.
|
| [20] |
Belfiore MP, Urraro F, Grassi R, et al. Artificial intelligence to codify lung CT in Covid-19 patients. Radiol Med, 2020,125(5):500-504. doi: 10.1007/s11547-020-01195-x.
doi: 10.1007/s11547-020-01195-x
URL
pmid: 32367319
|
| [21] |
Bai HX, Wang R, Xiong Z, et al. AI Augmentation of Radiolo-gist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT[J/OL]. Radiology, 2020:201491. [2020-06-26]. doi: 10.1148/radiol.2020201491. Online ahead of print.
|
| [22] |
Murphy K, Smits H, Knoops AJG, et al. COVID-19 on the Chest Radiograph: A Multi-Reader Evaluation of an AI System[J/OL]. Radiology, 2020:201874. [2020-06-26]. doi: 10.1148/radiol.2020201874.Online ahead of print.
|
| [23] |
Amyar A, Modzelewski R, Ruan S. Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation[J/OL]. MedRxiv, 2020. [2020-06-26]. doi: https://doi.org/10.1101/2020.04.16.20064709. Online ahead of print.
|
| [24] |
Wang S, Kang B, Ma J, et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)[J/OL]. MedRxiv, 2020. [2020-06-26]. doi: https://doi.org/10.1101/2020.02.14.20023028. Online ahead of print.
|
| [25] |
Kumar S, Mishra S, Kumar Singh S, et al. Deep Transfer Learning-based COVID-19 prediction using Chest X-rays[J/OL]. MedRxiv 2020. [2020-06-26]. doi: https://doi.org/10.1101/2020.05.12.20099937.Online ahead of print.
|
| [26] |
Razzak I, Naz S, Rehman A, et al. Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning[J/OL]. MedRxiv, 2020. [2020-06-26]. doi: https://doi.org/10.1101/2020.04.11.20054643. Online ahead of print.
|
| [27] |
Zu ZY, Jiang MD, Xu PP, et al. Coronavirus Disease 2019 (COVID-19): A Perspective from China[J/OL]. Radiology, 2020:200490. [2020-06-26]. doi: 10.1148/radiol.2020200490. Online ahead of print.
|
| [28] |
Yu N, Shen C, Yu Y, et al. Lung involvement in patients with coronavirus disease-19 (COVID-19): a retrospective study based on quantitative CT findings[J/OL]. Chin J Acad Radiol, 2020:1-6. [2020-06-26]. doi: 10.1007/s42058-020-00034-2. Online ahead of print.
|
| [29] |
He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition// IEEE Computer Society. Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas: IEEE Computer Society, 2016: 770-778.
|
| [30] |
Murphy K, Habib SS, Zaidi SMA, et al. Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system. Sci Rep, 2020,10(1):5492. doi: 10.1038/s41598-020-62148-y.
URL
pmid: 32218458
|
| [31] |
Shi H, Han X, Jiang N, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis, 2020,20(4):425-434. doi: 10.1016/S1473-3099(20)30086-4.
URL
pmid: 32105637
|
| [32] |
Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, et al. Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis, 2020,34:101623. doi: 10.1016/j.tmaid.2020.101623.
URL
pmid: 32179124
|
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