Acknowledgement
We want to thank Ullaskrishnan Poikavilla from Siemens Healthineers, USA, for installing the algorithm prototype at our medical center. Additionally, we appreciate the great support of our research team, namely Rita Achermann, Ivan Nesic, Joshy Cyriac, and Bram Stieltjes.
References
- Johns Hopkins University. COVID-19 Map - Johns Hopkins Coronavirus Resource Center. Coronavirus.jhu.edu Web site. https://coronavirus.jhu.edu/map.html. Accessed May 26, 2020
- Epidemiology Working Group for NCIP Epidemic Response, Chinese Center for Disease Control and Prevention. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua Liu Xing Bing Xue Za Zhi 2020;41:145-151
- Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis 2020;20:669-677 https://doi.org/10.1016/S1473-3099(20)30243-7
- Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ; HLH Across Speciality Collaboration, UK. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 2020;395:1033-1034 https://doi.org/10.1016/S0140-6736(20)30628-0
- American College of Radiology. ACR recommendations for the use of chest radiography and computed tomography (CT) for suspected COVID-19 infection. Acr.org Web site. https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection. Published 2020. Accessed August 6, 2020
- Hwang EJ, Kim H, Yoon SH, Goo JM, Park CM. Implementation of a deep learning-based computer-aided detection system for the interpretation of chest radiographs in patients suspected for COVID-19. Korean J Radiol 2020;21:1150-1160 https://doi.org/10.3348/kjr.2020.0536
- Sun D, Li X, Guo D, Wu L, Chen T, Fang Z, et al. CT quantitative analysis and its relationship with clinical features for assessing the severity of patients with COVID-19. Korean J Radiol 2020;21:859-868 https://doi.org/10.3348/kjr.2020.0293
- Li K, Fang Y, Li W, Pan C, Qin P, Zhong Y, et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol 2020;30:4407-4416 https://doi.org/10.1007/s00330-020-06817-6
- Zhang R, Ouyang H, Fu L, Wang S, Han J, Huang K, et al. CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city. Eur Radiol 2020;30:4417-4426 https://doi.org/10.1007/s00330-020-06854-1
- Liu Z, Jin C, Wu CC, Liang T, Zhao H, Wang Y, et al. Association between initial chest CT or clinical features and clinical course in patients with coronavirus disease 2019 pneumonia. Korean J Radiol 2020;21:736-745 https://doi.org/10.3348/kjr.2020.0171
- Wan S, Li M, Ye Z, Yang C, Cai Q, Duan S, et al. CT manifestations and clinical characteristics of 1115 patients with coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Acad Radiol 2020;27:910-921 https://doi.org/10.1016/j.acra.2020.04.033
- Li M, Lei P, Zeng B, Li Z, Yu P, Fan B, et al. Coronavirus disease (COVID-19): spectrum of CT findings and temporal progression of the disease. Acad Radiol 2020;27:603-608 https://doi.org/10.1016/j.acra.2020.03.003
- Zheng Y, Xiao A, Yu X, Zhao Y, Lu Y, Li X, et al. Development and validation of a prognostic nomogram based on clinical and CT features for adverse outcome prediction in patients with COVID-19. Korean J Radiol 2020;21:1007-1017 https://doi.org/10.3348/kjr.2020.0485
- Yin X, Min X, Nan Y, Feng Z, Li B, Cai W, et al. Assessment of the severity of coronavirus disease: quantitative computed tomography parameters versus semiquantitative visual score. Korean J Radiol 2020;21:998-1006 https://doi.org/10.3348/kjr.2020.0423
- Park B, Park J, Lim JK, Shin KM, Lee J, Seo H, et al. Prognostic implication of volumetric quantitative ct analysis in patients with COVID-19: a multicenter study in Daegu, Korea. Korean J Radiol 2020;21:1256-1264 https://doi.org/10.3348/kjr.2020.0567
- Driggin E, Madhavan MV, Bikdeli B, Chuich T, Laracy J, Biondi-Zoccai G, et al. Cardiovascular considerations for patients, health care workers, and health systems during the COVID-19 pandemic. J Am Coll Cardiol 2020;75:2352-2371 https://doi.org/10.1016/j.jacc.2020.03.031
- Chaganti S, Grenier P, Balachandran A, Chabin G, Cohen S, Flohr T, et al. Automated quantification of CT patterns associated with COVID-19 from chest CT. Radiology: Artificial Intelligence 2020;2:e200048
- Ali A, Balachandran A, Vishwanath RS, Barthur A, Wichmann JL, Cimen S, et al. Evaluation of a deep learning based aortic diameter quantification system against multi-reader consensus measurement. Proceedings 2020 European Congress of Radiology (ECR); 2020 Jul 15-19; Vienna, Austria: European Society of Radiology
- Martin SS, van Assen M, Rapaka S, Hudson HT Jr, Fischer AM, Varga-Szemes A, et al. Evaluation of a deep learning-based automated CT coronary artery calcium scoring algorithm. JACC Cardiovasc Imaging 2020;13:524-526 https://doi.org/10.1016/j.jcmg.2019.09.015
- DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837-845 https://doi.org/10.2307/2531595
- Tan C, Huang Y, Shi F, Tan K, Ma Q, Chen Y, et al. C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early. J Med Virol 2020;92:856-862 https://doi.org/10.1002/jmv.25871
- Lyu P, Liu X, Zhang R, Shi L, Gao J. The performance of chest CT in evaluating the clinical severity of COVID-19 pneumonia: identifying critical cases based on CT characteristics. Invest Radiol 2020;55:412-421 https://doi.org/10.1097/RLI.0000000000000689
- Francone M, Iafrate F, Masci GM, Coco S, Cilia F, Manganaro L, et al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur Radiol 2020;30:6808-6817 https://doi.org/10.1007/s00330-020-07033-y
- Li B, Yang J, Zhao F, Zhi L, Wang X, Liu L, et al. Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China. Clin Res Cardiol 2020;109:531-538 https://doi.org/10.1007/s00392-020-01626-9
- Ruch Y, Kaeuffer C, Ohana M, Labani A, Fabacher T, Bilbault P, et al. CT lung lesions as predictors of early death or ICU admission in COVID-19 patients. Clin Microbiol Infect 2020;26:1417.e5-1417.e8 https://doi.org/10.1016/j.cmi.2020.07.030
- Kim YC, Chung Y, Choe YH. Automatic localization of anatomical landmarks in cardiac MR perfusion using random forests. Biomed Signal Proces 2017;38:370-378 https://doi.org/10.1016/j.bspc.2017.07.001
- Huang L, Han R, Ai T, Yu P, Kang H, Tao Q, et al. Serial quantitative chest CT assessment of COVID-19: a deep learning approach. Radiology: Cardiothoracic Imaging 2020;2:e200075
- National Health Commission & National Administration of Traditional Chinese Medicine. Diagnosis and treatment protocol for novel coronavirus pneumonia. Chin Med J (Engl) 2020;133:1087-1095 https://doi.org/10.1097/CM9.0000000000000819
- Zheng YY, Ma YT, Zhang JY, Xie X. COVID-19 and the cardiovascular system. Nat Rev Cardiol 2020;17:259-260 https://doi.org/10.1038/s41569-020-0360-5