1. Zhai, P, Ding, Y, Wu, X, et al. The epidemiology, diagnosis and treatment of COVID-19. Int J Antimicrob Agents 2020; 55: 105955.
Google Scholar | Crossref | Medline2. Carvalho, TA, Boschiero, MN, Marson, FAL. COVID-19 in Brazil: 150,000 deaths and the Brazilian underreporting. Diagn Microbiol Infect Dis 2021; 99(3): 115258.
Google Scholar | Crossref | Medline3. Murphy, K, Smits, H, Knoops, AJ, et al. COVID-19 on the chest radiograph: a multi-reader evaluation of an AI system. Radiology 2020; 296: E166–E172.
Google Scholar | Crossref | Medline4. Long, C, Xu, H, Shen, Q, et al. Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT? Eur J Radiol 2020; 126: 108961.
Google Scholar | Crossref | Medline5. Phua, J, Weng, L, Ling, L, et al. Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations. Lancet Respir Med 2020; 8: 506–517.
Google Scholar | Crossref | Medline6. Vandenberg, O, Martiny, D, Rochas, O, et al. Considerations for diagnostic COVID-19 tests. Nat Rev Microbiol 2020; 19: 171–183.
Google Scholar | Crossref | Medline7. Caetano, R, Silva, AB, Guedes, ACCM, et al. Desafios e oportunidades para telessaúde em tempos da pandemia pela COVID-19: uma reflexão sobre os espaços e iniciativas no contexto brasileiro. Cad Saúde Pública 2020; 36: e00088920.
Google Scholar | Crossref | Medline8. Kalyanpur, A . The role and importance of teleradiology in the setting of corornavirus (COVID-19) infection, https://telradsol.com/role-and-importance-of-teleradiology-in-coronavirus-covid19-setting/ (2020, accessed 6 April 2020).
Google Scholar9. Rajpurkar, P, Irvin, J, Zhu, K, et al. Chexnet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv Preprint arXiv:1711.05225, 2017.
Google Scholar10. Krause, J, Gulshan, V, Rahimy, E, et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 2018; 125(8): 1264–1272.
Google Scholar | Crossref | Medline11. Wu, N, Phang, J, Park, J, et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging 2019; 39(4): 1184–1194.
Google Scholar | Crossref | Medline12. Chen, A, Karwoski, RA, Gierada, DS, et al. Quantitative CT analysis of diffuse lung disease. Radiographics 2020; 40(1): 28–43.
Google Scholar | Crossref | Medline13. Driver, CN, Bowles, BS, Bartholmai, BJ, et al. Artificial intelligence in radiology: a call for thoughtful application. Clin Transl Sci 2020; 13(2): 216–218.
Google Scholar | Crossref | Medline14. Esteva, A, Robicquet, A, Ramsundar, B, et al. A guide to deep learning in healthcare. Nat Med 2019; 25(1): 24–29.
Google Scholar | Crossref | Medline15. Reig, B, Heacock, L, Geras, KJ, et al. Machine learning in breast MRI. J Magn Reson Imaging 2020; 52(4): 998–1018.
Google Scholar | Crossref | Medline16. Lui, YW, Geras, K, Block, KT, et al. How to implement AI in the clinical enterprise: opportunities and lessons learned. J Am Coll Radiol 2020; 17(11): 1394–1397.
Google Scholar | Crossref | Medline17. Ng, MY, Lee, EY, Yang, J, et al. Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol Cardiothorac Imaging 2020; 2(1): e200034.
Google Scholar | Crossref | Medline18. Liu, H, Liu, F, Li, J, et al. Clinical and CT imaging features of the COVID-19 pneumonia: focus on pregnant women and children. J Infect 2020; 80(5): e7–e13.
Google Scholar | Crossref | Medline19. Wang, D, Mo, J, Zhou, G, et al. An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images. PLoS One 2020; 15(11): e0242535.
Google Scholar | Crossref | Medline20. Wang, S, Kang, B, Ma, J, et al. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol. Epub ahead of print 24 February 2021. DOI: 10.1007/s00330-021-07715-1.
Google Scholar | Crossref21. Wang, L, Lin, ZQ, Wong, A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 2020; 10(1): 19549.
Google Scholar | Crossref | Medline22. Jain, R, Gupta, M, Taneja, S, et al. Deep learning based detection and analysis of COVID-19 on chest X-ray images. Appl Intell 2021; 51: 1690–1700.
Google Scholar | Crossref23. Khan, AI, Shah, JL, Bhat, MM. CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput Methods Programs Biomed 2020; 196: 105581.
Google Scholar | Crossref | Medline24. Narin, A, Kaya, C, Pamuk, Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv Preprint arXiv:2003.10849, 2020.
Google Scholar25. Ucar, F, Korkmaz, D. COVIDiagnosis-Net: deep Bayes-Squeezenet based diagnostic of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses 2020; 140: 109761.
Google Scholar | Crossref | Medline26. Li, L, Qin, L, Xu, Z, et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 2020; 296(2): E65–E71.
Google Scholar | Crossref | Medline27. Ozturk, T, Talo, M, Yildirim, EA, et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020; 121: 103792.
Google Scholar | Crossref | Medline28. Wang, 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: 2000775.
Google Scholar | Crossref | Medline29. Zhang, K, Liu, X, Shen, J, et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 2020; 181: 1423–1433.e11.
Google Scholar | Crossref30. Singh, D, Kumar, V, Kaur, M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. Eur J Clin Microbiol Infect Dis 2020; 39: 1379–1389.
Google Scholar | Crossref | Medline31. Hemdan, EED, Shouman, MA, Karar, ME. Covidx-net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv Preprints arXiv:2003.10849, 2020.
Google Scholar32. Shamout, FE, Shen, Y, Wu, N, et al. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. arXiv Preprint arXiv:2008.01774, 2020.
Google Scholar33. Brewer, EA . Kubernetes and the path to cloud native. In: Proceedings of the sixth ACM symposium on cloud computing, Hawaii, US, 2015, pp.167–167.
Google Scholar | Crossref34. Tan, M, Le, QV. Efficientnet: rethinking model scaling for convolutional neural networks. arXiv Preprint arXiv:1905.11946, 2019.
Google Scholar35. He, K, Zhang, X, Ren, S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 2016, pp.770–778.
Google Scholar | Crossref36. Tan, M, Pang, R, Le, QV. Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, 2020, pp.10781–10790.
Google Scholar | Crossref37. Ramachandran, P, Zoph, B, Le, QV. Searching for activation functions. arXiv Preprint arXiv:1710.05941, 2017.
Google Scholar38. Chollet, F . Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Hawaii, US, 2017, pp.1251–1258.
Google Scholar | Crossref39. He, K, Zhang, X, Ren, S, et al. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, Santiago, Chile, 2015, pp.1026–1034. New York: IEEE.
Google Scholar | Crossref