Open Access
Issue
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
Article Number 05004
Number of page(s) 14
Section Epidemiology
DOI https://doi.org/10.1051/e3sconf/202344805004
Published online 17 November 2023
  1. Jeffrey Papp, Quality Management In The Imaging Series, Sixth. St. Louis, Missouri: Elseiver, 2019. [Google Scholar]
  2. L. P. Busby, J. L. Courtier, and C. M. Glastonbury, “Bias in radiology: The how and why of misses and misinterpretations,” Radiographics, vol. 38, no. 1, pp. 236–247, Jan. 2018, doi: 10.1148/rg.2018170107. [CrossRef] [PubMed] [Google Scholar]
  3. Mesfin Z., Elias K., and Melkamu B., “Analysis and Economic Implication of X-Ray Film Reject in Diagnostic Radiology Department of Jimma University Specialized Hospital, Southwest Ethiopia,” Ethiop J Health Sci., vol. 27, no. 4, pp. 421–426, Jul. 2017, doi: http://dx.doi.org/10.4314/ejhs.v27i4.13. [CrossRef] [PubMed] [Google Scholar]
  4. K. J. Little et al., “Unified Database for Rejected Image Analysis Across Multiple Vendors in Radiography,” Journal of the American College of Radiology, vol. 14, no. 2, pp. 208–216, Feb. 2017, doi: 10.1016/j.jacr.2016.07.011. [CrossRef] [PubMed] [Google Scholar]
  5. A. A. Almalki, R. A. Manaf, and N. M. Noor, “A Systematic Review on repetition Rate of Routine Digital Radiography,” Feb. 2017. [Online]. Available: http://www.journalcra.com [Google Scholar]
  6. A. Fathi, N. Fathalrahman Al, G. Afrah, W. Nehad, M. Zeinab, and E. Samah Tag, “X-Ray Film Reject Analysis in Radiology Departments of Port Sudan Hospitals,” International Journal of Radiology and Imaging Techniques, vol. 7, no. 1, Jan. 2021, doi: 10.23937/2572-3235.1510072. [CrossRef] [Google Scholar]
  7. M. Yusmiadil P M Y, N L. A. Rahman, A. A. Ahmad Asri, N. I. Othman, and I. W. Mokhtar, “Repeat analysis of intraoral digital imaging performed by undergraduated students using a complementary metal oxide semiconductor sensor: An institutional case study,” pp. 233–239, 2017, doi: https://doi.org/10.5624/isd.2017.47.4.233. [Google Scholar]
  8. Aysegül Y., Mustafa T., and Ramazan Y., “Reject Analysis in Digital Radiography: A Prospective Study,” International Journal of Anatomy, Radiology and Surgery, vol. 7, no. 4, pp. 31–34, Oct. 2018, doi: DOI: 10.7860/IJARS/2018/37410:2436. [Google Scholar]
  9. N. Mercieca, J. L. Portelli, and H. Jadva-Patel, “Mammographic image reject rate analysis and cause – A National Maltese Study,” Radiography, vol. 23, no. 1, pp. 25–31, Feb. 2017, doi: 10.1016/j.radi.2016.07.004. [CrossRef] [PubMed] [Google Scholar]
  10. H. Precht, J. Hansson, C. Outzen, P. Hogg, and A. Tingberg, “Radiographers’ perspectives’ on Visual Grading Analysis as a scientific method to evaluate image quality,” Radiography, vol. 25, pp. S14–S18, Oct. 2019, doi: 10.1016/j.radi.2019.06.006. [CrossRef] [PubMed] [Google Scholar]
  11. E. Kjelle, A. K. Schanche, and L. Hafskjold, “To keep or reject, that is the question - A survey on radiologists and radiographers’ assessments of plain radiography images,” Radiography, vol. 27, no. 1, pp. 115–119, Feb. 2021, doi: 10.1016/j.radi.2020.06.020. [CrossRef] [PubMed] [Google Scholar]
  12. M. Uffmann and C. Schaefer-Prokop, “Digital radiography: The balance between image quality and required radiation dose,” Eur J Radiol, vol. 72, no. 2, pp. 202–208, 2009, doi: 10.1016/j.ejrad.2009.05.060. [CrossRef] [PubMed] [Google Scholar]
  13. R. Honea, M. Elissa Blado, and Y. Ma, “Is reject analysis necessary after converting to computed radiography?,” Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology, vol. 15 Suppl 1, pp. 41–52, 2002, doi: 10.1007/s10278-002-5028-7. [Google Scholar]
  14. S. L. Lau, A. S. H. Mak, W. T. Lam, C. K. Chau, and K. Y. Lau, “Reject analysis: A comparison of conventional film-screen radiography and computed radiography with PACS,” Radiography, vol. 10, no. 3, pp. 183–187, Aug. 2004, doi: 10.1016/j.radi.2004.03.014. [CrossRef] [Google Scholar]
  15. A A. Sadiq, M. Nike Miftaudeen, A. Mohammed, and S. Nigeria, “Reject-Repeat Analysis of Plan Radiographs as a Quality Indicator At University of Maiduguri Teaching Hospital (UMTH),” European Journal of Pharmaceutical and Medical Research, vol. 4, no. 2, pp. 188–191, 2017, [Online]. Available: www.ejpmr.com [Google Scholar]
  16. M. Usha, S. Bhargava, and S. Bhatt, “Reject Analysis in Conventional Radiography,” Nepalese Journal of Radiology, vol. 3, no. 2, pp. 65–67, Jan. 2014, doi: 10.3126/njr.v3i2.9612. [CrossRef] [Google Scholar]
  17. P. A. Clark and P. Hogg, “Reject/repeat analysis and the effect prior film viewing has on a department’s reject/repeat rate,” Radiography, vol. 9, no. 2, pp. 127–137, 2003, doi: 10.1016/S1078-8174(03)00036-1. [CrossRef] [Google Scholar]
  18. D. H. Foos, W. J. Sehnert, B. Reiner, E. L. Siegel, A. Segal, and D. L. Waldman, “Digital radiography reject analysis: Data collection methodology, results, and recommendations from an in-depth investigation at two hospitals,” J Digit Imaging, vol. 22, no. 1, pp. 89–98, Feb. 2009, doi: 10.1007/s10278-008-9112-5. [CrossRef] [PubMed] [Google Scholar]
  19. A. K. Jones, R. Polman, C. E. Willis, and S. J. Shepard, “One year’s results from a server-based system for performing reject analysis and exposure analysis in computed radiography,” J Digit Imaging, vol. 24, no. 2, pp. 243–255, Apr. 2011, doi: 10.1007/s10278-009-9236-2. [CrossRef] [PubMed] [Google Scholar]
  20. F. Fintelmann et al., “Repeat Rates in Digital Chest Radiography and Strategies for Improvement,” 2012. [Online]. Available: www.thoracicimaging.com [Google Scholar]
  21. W. S. Tzeng, K. M. Kuo, C. F. Liu, H. C. Yao, C. Y. Chen, and H. W. Lin, “Managing repeat digital radiography images - A systematic approach and improvement,” J Med Syst, vol. 36, no. 4, pp. 2697–2704, Aug. 2012, doi: 10.1007/s10916-011-9744-8. [CrossRef] [PubMed] [Google Scholar]
  22. J. Owusu-Banahene, E.O. Darko, F. Hasford, E.K. Addison, and O. A. J, “Film reject analysis and image quality in diagnostic Radiology Department of a Teaching hospital in Ghana,” Journal of Radiation Research and Applied Science, pp. 589–594, 2019, doi: http://dx.doi.org/10.1016/j.jrras.2014.09.12. [Google Scholar]
  23. B. Hofmann, T. B. Rosanowsky, C. Jensen, and K. H. C. Wah, “Image rejects in general direct digital radiography,” Acta Radiol Open, vol. 4, no. 10, p. 205846011560433, Oct. 2015, doi: 10.1177/2058460115604339. [CrossRef] [Google Scholar]
  24. M. Y. P. M. Yusof, N. L. Abdul Rahman, A. A. Ahmad Asri, N. I. Othman, and I. W. Mokhtar, “Repeat analysis of intraoral digital imaging performed by undergraduate students using a complementary metal oxide semiconductor sensor: An institutional case study,” Imaging Sci Dent, vol. 47, no. 4, pp. 233–239, 2017, doi: 10.5624/isd.2017.47.4.233. [CrossRef] [PubMed] [Google Scholar]
  25. O. Alahmadi, A. Alrehaili, and M. Gameraddin, “Evaluation of Reject Analysis of Chest Radiographs in Diagnostic Radiology,” American Journal of Diagnostic Imaging, vol. 5, no. 1, p. 4, 2019, doi: 10.5455/ajdi.20180830110208. [CrossRef] [Google Scholar]
  26. K. J. Little et al., “Unified Database for Rejected Image Analysis Across Multiple Vendors in Radiography,” Journal of the American College of Radiology, vol. 14, no. 2, pp. 208–216, Feb. 2017, doi: 10.1016/j.jacr.2016.07.011. [CrossRef] [PubMed] [Google Scholar]
  27. S. Atkinson, M. Neep, and D. Starkey, “Reject rate analysis in digital radiography: an Australian emergency imaging department case study,” J Med Radiat Sci, vol. 67, no. 1, pp. 72–79, Mar. 2020, doi: 10.1002/jmrs.343. [CrossRef] [PubMed] [Google Scholar]
  28. Khalid A. Alyousef, Satha Alkahtani, Raghad Alessa, and Hajar Alruweili, “Radiograph Reject Analysis in a Large Tertiary Care Hospital in Riyadh, Saudi Arabia,” Global Journal on Quality and Safety in Healhcare, 2019. [Google Scholar]
  29. B. Stephenson-Smith, M. J. Neep, and P. Rowntree, “Digital radiography reject analysis of examinations with multiple rejects: an Australian emergency imaging department clinical audit,” J Med Radiat Sci, vol. 68, pp. 245–252, 2021, doi: 10.1002/jmrs.468. [CrossRef] [PubMed] [Google Scholar]
  30. Y. Alashban, N. Shubayr, A. A. Alghamdi, S. A. Alghamdi, and S. Boughattas, “An assessment of image reject rates for digital radiography in Saudi Arabia: A cross-sectional study,” J Radiat Res Appl Sci, vol. 15, no. 1, pp. 219–223, Mar. 2022, doi: 10.1016/j.jrras.2022.01.023. [Google Scholar]
  31. D. S. Joseph, U. Sani, D. Z. Joseph, and U. Abubakar, “Evaluation of Rejected Images in Computerized Radiography(CR),” Pakistan Journal of Radiology, vol. 31, no. 3, pp. 213–219, 2021. [Google Scholar]
  32. Y. Alashban, N. Shubayr, A. A. Alghamdi, S. A. Alghamdi, and S. Boughattas, “An assessment of image reject rates for digital radiography in Saudi Arabia: A cross- sectional study,” J Radiat Res Appl Sci, vol. 15, no. 1, pp. 219–223, Mar. 2022, doi: 10.1016/j.jrras.2022.01.023. [Google Scholar]
  33. J. Owusu-Banahene, E. O. Darko, F. Hasford, E. K. Addison, and J. O. Asirifi, “Film reject analysis and image quality in diagnostic Radiology Department of a Teaching hospital in Ghana,” J Radiat Res Appl Sci, vol. 7, no. 4, pp. 589–594, Oct. 2014, doi: 10.1016/j.jrras.2014.09.012. [Google Scholar]
  34. S. Atkinson, M. Neep, and D. Starkey, “Reject rate analysis in digital radiography: an Australian emergency imaging department case study,” J Med Radiat Sci, vol. 67, no. 1, pp. 72–79, Mar. 2020, doi: 10.1002/jmrs.343. [CrossRef] [PubMed] [Google Scholar]
  35. S. L. Lau, A. S. H. Mak, W. T. Lam, C. K. Chau, and K. Y. Lau, “Reject analysis: A comparison of conventional film-screen radiography and computed radiography with PACS,” Radiography, vol. 10, no. 3, pp. 183–187, Aug. 2004, doi: 10.1016/j.radi.2004.03.014. [CrossRef] [Google Scholar]
  36. D. Waaler and B. Hofmann, “Image rejects/retakes-radiographic challenges,” Radiat Prot Dosimetry, vol. 139, no. 1–3, pp. 375–379, Feb. 2010, doi: 10.1093/rpd/ncq032. [CrossRef] [PubMed] [Google Scholar]
  37. J. Nol, G. Isouard, and J. Mirecki, “Digital repeat analysis; setup and operation,” J Digit Imaging, vol. 19, no. 2, pp. 159–166, Jun. 2006, doi: 10.1007/S10278-005-8733-1. [CrossRef] [PubMed] [Google Scholar]
  38. M. Hardy and H. Harvey, “Artificial intelligence in diagnostic imaging: impact on the radiography profession,” Br J Radiol, vol. 93, no. 1108, p. 20190840, Apr. 2020, doi: 10.1259/bjr.20190840. [CrossRef] [PubMed] [Google Scholar]
  39. J. S. Whaley, B. D. Pressman, J. R. Wilson, L. Bravo, W. J. Sehnert, and D. H. Foos, “Investigation of the variability in the assessment of digital chest x-ray image quality,” J Digit Imaging, vol. 26, no. 2, pp. 217–226, 2013, doi: 10.1007/s10278-012-9515-1. [CrossRef] [PubMed] [Google Scholar]
  40. M. Moradi et al., “Artificial intelligence for point of care radiograph quality assessment,” SPIE-Intl Soc Optical Eng, Mar. 2019, p. 128. doi: 10.1117/12.2513092. [Google Scholar]

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