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