Page 68 - Read Online
P. 68
Tovar et al. Art Int Surg 2023;3:14-26 https://dx.doi.org/10.20517/ais.2022.38 Page 26
62. Chen J, Yang R, Lu Y, Xia Y, Zhou H. Diagnostic accuracy of endoscopic ultrasound-guided fine-needle aspiration for solid
pancreatic lesion: a systematic review. J Cancer Res Clin Oncol 2012;138:1433-41. DOI PubMed
63. Hayashi T, Ishiwatari H, Yoshida M, et al. Rapid on-site evaluation by endosonographer during endoscopic ultrasound-guided fine
needle aspiration for pancreatic solid masses. J Gastroenterol Hepatol 2013;28:656-63. DOI PubMed
64. Alston E, Bae S, Eltoum IA. Atypical cytologic diagnostic category in EUS-FNA of the pancreas: follow-up, outcomes, and predictive
models. Cancer Cytopathol 2014;122:428-34. DOI PubMed
65. Savoy AD, Raimondo M, Woodward TA, et al. Can endosonographers evaluate on-site cytologic adequacy? A comparison with
cytotechnologists. Gastrointest Endosc 2007;65:953-7. DOI PubMed
66. Del Castillo E, Meier R, Chung M, et al. The microbiomes of pancreatic and duodenum tissue overlap and are highly subject specific
but differ between pancreatic cancer and noncancer subjects. Cancer Epidemiol Biomarkers Prev 2019;28:370-83. DOI PubMed
PMC
67. Sheller MJ, Edwards B, Reina GA, et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing
patient data. Sci Rep 2020;10:12598. DOI PubMed PMC
68. Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ Digit Med 2020;3:119. DOI PubMed PMC
69. Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W. Federated learning of predictive models from federated Electronic
Health Records. Int J Med Inform 2018;112:59-67. DOI PubMed PMC
70. Qayyum A, Ahmad K, Ahtazaz Ahsan M, Al-Fuqaha A, Qadir J. Collaborative Federated Learning for Healthcare: Multi-Modal
COVID-19 Diagnosis at the Edge. IEEE Open J Comput Soc 2022;3:172-84. DOI
71. Maitra A. A clinical validation center for early detection of pancreatic cancer. Available from: https://grantome.com/grant/NIH/U01-
CA200468-02 [Last accessed on 17 Mar 2023].
72. Koay EJ, Lee Y, Cristini V, et al. A visually apparent and quantifiable ct imaging feature identifies biophysical subtypes of pancreatic
ductal adenocarcinoma. Clin Cancer Res 2018;24:5883-94. DOI PubMed PMC
73. Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med 2019;25:37-43. DOI PubMed PMC
74. Grote T, Berens P. On the ethics of algorithmic decision-making in healthcare. J Med Ethics 2020;46:205-11. DOI PubMed PMC
75. van de Sande D, Van Genderen ME, Smit JM, et al. Developing, implementing and governing artificial intelligence in medicine: a
step-by-step approach to prevent an artificial intelligence winter. BMJ Health Care Inform 2022;29:e100495. DOI PubMed PMC
76. Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence
algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 2019;20:405-10. DOI
PubMed PMC
77. Norgeot B, Quer G, Beaulieu-Jones BK, et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM
checklist. Nat Med 2020;26:1320-4. DOI PubMed PMC