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Machine Learning Models Accurately Interpret Liver Histology and Are Associated With
Disease Progression in Patients With Primary Sclerosing Cholangitis
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EASL - The Digital International Liver Congress, 27-29 August 2020
Nathaniel Travis,1 Vincent Billaut,1 Harsha Pokkalla,1 Kishalve Pethia,1 Oscar Carrasco-Zevallos,1 Benjamin Glass,1 Amaro Taylor-Weiner,1 Christopher L. Bowlus,2 Atsushi Tanaka,3 Douglas Thorburn,4 Xiaomin Lu,5
Ryan Huss,5 Chuhan Chung,5 G. Mani Subramanian,5 Robert P. Myers,5 Andrew J. Muir,6 Kris V. Kowdley,7 Zachary Goodman,8 Aditya Khosla,1 Andrew Beck,1 Murray Resnick,1 Ilan Wapinski,1 Michael H. Trauner,9 Cynthia Levy10
1PathAI, Inc., Boston, Massachusetts, USA; 2University of California, Davis, USA; 3Teikyo University School of Medicine, Tokyo, Japan; 4UCL Institute for Liver & Digestive Health, Royal Free Hospital, London, UK; 5Gilead Sciences, Inc., Foster City, California;
6Duke Clinical Research Institute, Durham, North Carolina, USA; 7Inova Fairfax Hospital, Falls Church, Virginia, USA; 8Liver Institute Northwest, Seattle, Washington, USA; 9Medical University of Vienna, Austria; 10University of Miami, Coral Gables, Florida, USA
References: 1. Eaton JE, et al. Gastroenterology 2013;145;3:521-36; 2. Lazaridis KN, LaRusso NF. N Engl J Med 2016;22;375:1161-70; 3. DeVries EM, et al. Hepatology 2017;65;3:907-19; 4. Pokkalla H, et al. AASLD 2019, abstr 187. Acknowledgments: We extend our thanks to the patients and their families. This study was funded by Gilead Sciences, Inc.
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