Articles

Artificial Intelligence in Healthcare

AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone

Academic Edition, Chapter 13 - References/Further Reading

 

  1. Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI: Journal of the National Cancer Institute. 2019 Sep 1;111(9):916–22.
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  3. Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, et al. Preparing medical imaging data for machine learning. Radiology. 2020 Feb 18;295(1):4–15.
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  8. Dikici E, Bigelow M, Prevedello LM, White RD, Erdal BS. Integrating AI into radiology workflow: levels of research, production, and feedback maturity. J Med Imaging (Bellingham). 2020;7(1):016502.
  9. 10 key uses for AI in radiology that don’t involve interpretation n.d. https://www.aiin.healthcare/topics/medical-imaging/10-key-uses-ai-radiology-interpretation-imaging (accessed July 25, 2021).
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  11. Richardson ML, Garwood ER, Lee Y, Li MD, Lo HS, Nagaraju A, Nguyen XV, Probyn L, Rajiah P, Sin J, Wasnik AP. Noninterpretive uses of artificial intelligence in radiology. Academic Radiology. 2020 Feb 12.
  12. H. McCollough and S. Leng, “Use of artificial intelligence in computed tomography dose optimisation,” Ann. ICRP, vol. 49, no. 1_suppl, pp. 113–125, 2020, doi: 10.1177/0146645320940827.
  13. Saltybaeva N, Schmidt B, Wimmer A, Flohr T, Alkadhi H. Precise and Automatic Patient Positioning in Computed Tomography: Avatar Modeling of the Patient Surface Using a 3-Dimensional Camera. Invest Radiol. 2018 Nov;53(11):641-646.
  14. Booij R, van Straten M, Wimmer A, Budde RPJ. Automated patient positioning in CT using a 3D camera for body contour detection: accuracy in pediatric patients. Eur Radiol. 2021 Jan;31(1):131-138.
  15. Hinzpeter et al., “CT angiography of the aorta: Contrast timing by using a fixed versus a patientspecific trigger delay,” Radiology, vol. 291, no. 2, pp. 531–538, 2019
  16. Gutjahr et al., “Individualized Delay for Abdominal Computed Tomography Angiography BolusTracking Based on Sequential Monitoring: Increased Aortic Contrast Permits Decreased Injection Rate and Lower Iodine Dose,” J. Comput. Assist. Tomogr., vol. 43, no. 4, pp. 612–618, 2019, doi: 10.1097/RCT.0000000000000874.
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  18. Feng & Chen, Yang & Liao, Peixi & Zhou, Jiliu & Wang, Ge. (2017). Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural .IEEE Transactions on Medical Imaging. 36. 2524-2535.
  19. Widomska Justyna, “Low Dose CT Image Denoising,” Physiol. Behav., vol. 176, no. 5, . 139–148,2017, doi: 10.1109/TMI.2018.2827462.Low.
  20. Yi and P. Babyn, “Sharpness-aware Low dose CT denoising using conditional generative adversarial network,” arXiv, 2017.
  21. D. Missert, L. Yu, S. Leng, J. G. Fletcher, and C. H. McCollough, “Synthesizing images from multiple kernels using a deep convolutional neural network,” Med. Phys., vol. 47, no. 2, pp. 422–430, 2020, doi: 10.1002/mp.13918.
  22. P. Favazza, A. Ferrero, L. Yu, S. Leng, and K. L. McMillan, “Use of a channelized Hotelling observer to assess CT image quality and optimize dose reduction for iteratively reconstructed images,” J. Med. Imaging, vol. 4, no. 03, p. 1, 2017, doi: 10.1117/1.jmi.4.3.031213.
  23. Wolterink JM, Leiner T, Viergever MA, Isgum I. Generative Adversarial Networks for Noise Reduction in Low-Dose CT. IEEE Trans Med Imaging. 2017 Dec;36(12):2536-2545
  24. Akagi et al., “Deep learning reconstruction improves image quality of abdominal ultra-highresolution CT,” Eur. Radiol., vol. 29, no. 11, pp. 6163–6171, 2019, doi: 10.1007/s00330-019-06170-3.
  25. Kuo, Y. Y. Lin, R. C. Lee, C. J. Lin, Y. Y. Chiou, and W. Y. Guo, “Comparison of image quality from filtered back projection, statistical iterative reconstruction, and model-based iterative reconstruction algorithms in abdominal computed tomography,” Med. (United States), vol. 95, no. 31, 2016, doi: 10.1097/MD.0000000000004456.
  26. Euler et al., “Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages,” Eur. Radiol., vol. 27, no. 12, pp. 5252–5259, 2017, doi: 10.1007/s00330-017-4825-9.
  27. Higaki et al., “Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics,”Acad. Radiol., vol. 27, no. 1, pp. 82–87, 2020, doi: 10.1016/j.acra.2019.09.008.
  28. Zhang and E. Seeram, “The use of artificial intelligence in computed tomography image reconstruction - A literature review,” J. Med. Imaging Radiat. Sci., vol. 51, no. 4, pp. 671–677, 2020, doi: 10.1016/j.jmir.2020.09.001.
  29. Davnall et al., “Assessment of tumor heterogeneity: An emerging imaging tool for clinical practice?,” Insights Imaging, vol. 3, no. 6, pp. 573–589, 2012, doi: 10.1007/s13244-012-0196-6.
  30. Lambin et al., “Radiomics: The bridge between medical imaging and personalized medicine,” Nat. Rev. Clin. Oncol., vol. 14, no. 12, pp. 749–762, 2017, doi: 10.1038/nrclinonc.2017.141.
  31. Kolossváry, M. Kellermayer, B. Merkely, and P. Maurovich-Horvat, “Cardiac Computed Tomography Radiomics,” J. Thorac. Imaging, vol. 33, no. 1, pp. 26–34, 2018, doi: 10.1097/RTI.0000000000000268.
  32. W. Nance, C. Meenan, and P. G. Nagy, “The Future of the radiology information system,” Am. J. Roentgenol., vol. 200, no. 5, pp. 1064–1070, 2013, doi: 10.2214/AJR.12.10326
  33. Vasconcelos R, Vrtiska TJ, Foley TA, Macedo TA, Cardona JC, Williamson EE, McCollough CH, Fletcher JG. Reducing Iodine Contrast Volume in CT Angiography of the Abdominal Aorta Using Integrated Tube Potential Selection and Weight-Based Method Without Compromising Image Quality. AJR Am J Roentgenol. 2017 Mar;208(3):552-563.
  34. Columbus L. What’s new in gartner’s hype cycle for ai, 2020 [Internet]. Forbes. [cited 2021 Jan 22]. Available from: https://www.forbes.com/sites/louiscolumbus/2020/10/04/whats-new-in-gartners-hype-cycle-for-ai-2020/
  35. Kotter E, Ranschaert E. Challenges and solutions for introducing artificial intelligence (Ai) in daily clinical workflow. Eur Radiol. 2021 Jan 1;31(1):5–7.
  36. org, radboudumc. Radiology management, icu management, healthcare it, cardiology management, executive management [Internet]. HealthManagement. [cited 2021 Jan 22]. Available from: https://healthmanagement.org/c/hospital/issuearticle/how-to-integrate-ai-into-radiology-workflow