KJR Ways to Recognize Most Impactful Articles and Distinguished Reviewers

참고문헌

1. Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 2019;20:405-410
2. Goo HW, Goo JM. Dual-energy CT: new horizon in medical imaging. Korean J Radiol 2017;18:555-569
3. Yoon SH, Lee KH, Kim JY, Lee YK, Ko H, Kim KH, et al. Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea. Korean J Radiol 2020;21:494-500
4. Sun R, Liu H, Wang X. Mediastinal emphysema, giant bulla, and pneumothorax developed during the course of COVID-19 pneumonia. Korean J Radiol 2020;21:541-544
5. Wei J, Xu H, Xiong J, Shen Q, Fan B, Ye C, et al. 2019 novel coronavirus (COVID-19) pneumonia: serial computed tomography findings. Korean J Radiol 2020;21:501-504
6. Li D, Wang D, Dong J, Wang N, Huang H, Xu H, et al. False-negative results of real-time reverse-transcriptase polymerase chain reaction for severe acute respiratory syndrome coronavirus 2: role of deep-learning-based CT diagnosis and insights from two cases. Korean J Radiol 2020;21:505-508
7. Lin X, Gong Z, Xiao Z, Xiong J, Fan B, Liu J. Novel coronavirus pneumonia outbreak in 2019: computed tomographic findings in two cases. Korean J Radiol 2020;21:365-368
8. Park SH. What's new in the Korean Journal of Radiology in 2021. Korean J Radiol 2021;22:1-4
9. Park SH. Thanks to the reviewers of the Korean Journal of Radiology. Korean J Radiol 2021;22:5-8
10. Choe YH. Thanks to the reviewers of the Korean Journal of Radiology. Korean J Radiol 2020;21:1-3