Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Performing a Research Study Using Open-Source Deep Learning Models

  • Hyungjin Kim (Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital)
  • Received : 2023.09.06
  • Accepted : 2023.11.04
  • Published : 2024.03.01
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Abstract

Keywords

Acknowledgement

English editing of this article was done by the chatGPT (GPT-3.5; OpenAI, San Francisco, CA, USA).

References

  1. Morton L. Uphold the code: how complete, detailed and open code can enhance understanding, improve reproducibility, and change the shape of the research article [accessed on September 6, 2023]. Available at: https://theplosblog.plos.org/2022/05/uphold-the-code/
  2. Hwang EJ, Goo JM, Nam JG, Park CM, Hong KJ, Kim KH. Conventional versus artificial intelligence-assisted interpretation of chest radiographs in patients with acute respiratory symptoms in emergency department: a pragmatic randomized clinical trial. Korean J Radiol 2023;24:259-270
  3. Huh JE, Lee JH, Hwang EJ, Park CM. Effects of expertdetermined reference standards in evaluating the diagnostic performance of a deep learning model: a malignant lung nodule detection task on chest radiographs. Korean J Radiol 2023;24:155-165
  4. Yoo H, Kim EY, Kim H, Choi YR, Kim MY, Hwang SH, et al. Artificial intelligence-based identification of normal chest radiographs: a simulation study in a multicenter health screening cohort. Korean J Radiol 2022;23:1009-1018
  5. Lu MT, Raghu VK, Mayrhofer T, Aerts HJWL, Hoffmann U. Deep learning using chest radiographs to identify high-risk smokers for lung cancer screening computed tomography: development and validation of a prediction model. Ann Intern Med 2020;173:704-713
  6. Lee JH, Lee D, Lu MT, Raghu VK, Park CM, Goo JM, et al. Deep learning to optimize candidate selection for lung cancer CT screening: advancing the 2021 USPSTF recommendations. Radiology 2022;305:209-218
  7. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015;162:W1-W73
  8. Weiss J, Raghu VK, Bontempi D, Christiani DC, Mak RH, Lu MT, et al. Deep learning to estimate lung disease mortality from chest radiographs. Nat Commun 2023;14:2797
  9. Lee JH, Choi SH, Jung KJ, Goo JM, Yoon SH. High visceral fat attenuation and long-term mortality in a health check-up population. J Cachexia Sarcopenia Muscle 2023;14:1495-1507
  10. Lee YS, Hong N, Witanto JN, Choi YR, Park J, Decazes P, et al. Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment. Clin Nutr 2021;40:5038-5046
  11. Lee MH, Zea R, Garrett JW, Graffy PM, Summers RM, Pickhardt PJ. Abdominal CT body composition thresholds using automated AI tools for predicting 10-year adverse outcomes. Radiology 2023;306:e220574
  12. Pickhardt PJ, Summers RM, Garrett JW. Automated CT-based body composition analysis: a golden opportunity. Korean J Radiol 2021;22:1934-1937