Imaging Science in Dentistry

  • 제51권2호
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  • Pages.129-136
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  • 2021
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  • 2233-7822(pISSN)
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  • 2233-7830(eISSN)
대한영상치의학회 (Korean Academy of Oral and Maxillofacial Radiology)
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Effects of 1 year of training on the performance of ultrasonographic image interpretation: A preliminary evaluation using images of Sjogren syndrome patients

  • Kise, Yoshitaka (Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry) ;
  • Moystad, Anne (Institute of Clinical Dentistry, Faculty of Dentistry, University of Oslo) ;
  • Bjornland, Tore (Department of Oral Surgery and Oral Medicine, Institute of Clinical Dentistry, Faculty of Dentistry, University of Oslo) ;
  • Shimizu, Mayumi (Department of Oral and Maxillofacial Radiology, Kyushu University Hospital) ;
  • Ariji, Yoshiko (Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry) ;
  • Kuwada, Chiaki (Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry) ;
  • Nishiyama, Masako (Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry) ;
  • Funakoshi, Takuma (Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry) ;
  • Yoshiura, Kazunori (Department of Oral and Maxillofacial Radiology, Faculty of Dental Science, Kyushu University) ;
  • Ariji, Eiichiro (Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry)
  • 투고 : 2020.10.27
  • 심사 : 2020.12.05
  • 발행 : 2021.06.30
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초록

Purpose: This study investigated the effects of 1 year of training on imaging diagnosis, using static ultrasonography (US) salivary gland images of Sjögren syndrome patients. Materials and Methods: This study involved 3 inexperienced radiologists with different levels of experience, who received training 1 or 2 days a week under the supervision of experienced radiologists. The training program included collecting patient histories and performing physical and imaging examinations for various maxillofacial diseases. The 3 radiologists (observers A, B, and C) evaluated 400 static US images of salivary glands twice at a 1-year interval. To compare their performance, 2 experienced radiologists evaluated the same images. Diagnostic performance was compared between the 2 evaluations using the area under the receiver operating characteristic curve (AUC). Results: Observer A, who was participating in the training program for the second year, exhibited no significant difference in AUC between the first and second evaluations, with results consistently comparable to those of experienced radiologists. After 1 year of training, observer B showed significantly higher AUCs than before training. The diagnostic performance of observer B reached the level of experienced radiologists for parotid gland assessment, but differed for submandibular gland assessment. For observer C, who did not complete the training, there was no significant difference in the AUC between the first and second evaluations, both of which showed significant differences from those of the experienced radiologists. Conclusion: These preliminary results suggest that the training program effectively helped inexperienced radiologists reach the level of experienced radiologists for US examinations.

키워드

과제정보

We thank Helen Jeays, BDSc AE, from Edanz Group (https://en-author-services.edanzgroup.com/ac) for editing a draft of this manuscript.

참고문헌

  1. Ariji Y, Kimura Y, Hayashi N, Onitsuka T, Yonetsu K, Hayashi K, et al. Power Doppler sonography of cervical lymph nodes in patients with head and neck cancer. AJNR Am J Neuroradiol 1998; 19: 303-7.
  2. Li T, Jiang Z, Lu M, Zou S, Wu M, Wei T, et al. Computer-aided diagnosis system of thyroid nodules ultrasonography: diagnostic performance difference between computer-aided diagnosis and 111 radiologists. Medicine (Baltimore) 2020; 99: e20634. https://doi.org/10.1097/MD.0000000000020634
  3. Lee J, Kim S, Kang BJ, Kim SH, Park GE. Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions. Med Ultrason 2019; 21: 239-45. https://doi.org/10.11152/mu-1889
  4. Park HJ, Kim SM, Yun BL, Jang M, Kim B, Jang JY, et al. A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultra-sound: added value for the inexperienced breast radiologist. Medicine (Baltimore) 2019; 98: e14146. https://doi.org/10.1097/md.0000000000014146
  5. Becker AS, Mueller M, Stoffel E, Marcon M, Ghafoor S, Boss A. Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study. Br J Radiol 2018; 91: 20170576.
  6. Shimizu M, Okamura K, Yoshiura K, Ohyama Y, Nakamura S, Kinukawa N. Sonographic diagnostic criteria for screening Sjogren's syndrome. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2006; 102: 85-93. https://doi.org/10.1016/j.tripleo.2005.09.012
  7. Kise Y, Shimizu M, Ikeda H, Fujii T, Kuwada C, Nishiyama M, et al. Usefulness of a deep learning system for diagnosing Sjogren's syndrome using ultrasonography images. Dentomaxillofac Radiol 2020; 49: 20190348. https://doi.org/10.1259/dmfr.20190348
  8. Chalumeau-Lemoine L, Baudel JL, Das V, Arrive L, Noblinski B, Guidet B, et al. Results of short-term training of naive physicians in focused general ultrasonography in an intensive-care unit. Intensive Care Med 2009; 35: 1767-71. https://doi.org/10.1007/s00134-009-1531-3
  9. Syperda VA, Trivedi PN, Melo LC, Freeman ML, Ledermann EJ, Smith TM, et al. Ultrasonography in preclinical education: a pilot study. J Am Osteopath Assoc 2008; 108: 601-5.
  10. Counselman FL, Sanders A, Slovis CM, Danzl D, Binder LS, Perina DG. The status of bedside ultrasonography training in emergency medicine residency programs. Acad Emerg Med 2003; 10: 37-42. https://doi.org/10.1111/j.1553-2712.2003.tb01974.x
  11. Delzell PB, Boyle A, Schneider E. Dedicated training program for shoulder sonography: the results of a quality program reverberate with everyone. J Ultrasound Med 2015; 34: 1037-42. https://doi.org/10.7863/ultra.34.6.1037
  12. Fulton N, Buethe J, Gollamudi J, Robbin M. Simulation-based training may improve resident skill in ultrasound-guided biopsy. AJR Am J Roentgenol 2016; 207: 1329-33. https://doi.org/10.2214/AJR.16.16161
  13. Roark AA, Ebuoma LO, Ortiz-Perez T, Sepulveda KA, Severs FJ, Wang T, et al. Impact of simulation-based training on radiology trainee education in ultrasound-guided breast biopsies. J Am Coll Radiol 2018; 15: 1458-63. https://doi.org/10.1016/j.jacr.2017.09.016
  14. Fujibayashi T, Sugai S, Miyasaka N, Hayashi Y, Tsubota K. Revised Japanese criteria for Sjogren's syndrome (1999): availability and validity. Mod Rheumatol 2004; 14: 425-34. https://doi.org/10.3109/s10165-004-0338-x
  15. Vitali C, Bombardieri S, Jonsson R, Moutsopoulos HM, Alexander EL, Carsons SE, et al. Classification criteria for Sjogren's syndrome: a revised version of the European criteria proposed by the American-European Consensus Group. Ann Rheum Dis 2002; 61: 554-8. https://doi.org/10.1136/ard.61.6.554
  16. Izumi M, Eguchi K, Nakamura H, Nagataki S, Nakamura T. Premature fat deposition in the salivary glands associated with Sjogren syndrome: MR and CT evidence. AJNR Am J Neuroradiol 1997; 18: 951-8.
  17. Takagi Y, Sumi M, Sumi T, Ichikawa Y, Nakamura T. MR microscopy of the parotid glands in patients with Sjogren's syndrome: quantitative MR diagnostic criteria. AJNR Am J Neuroradiol 2005; 26: 1207-14.