A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH-Reference-Cited by-同舟云学术

A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH

Published:2021-06-24 Issue:1 Volume:74 Page:133-147
ISSN:0270-9139
Container-title:Hepatology
language:en
Short-container-title:Hepatology

Author:

Taylor‐Weiner Amaro¹,Pokkalla Harsha¹,Han Ling²,Jia Catherine²,Huss Ryan²,Chung Chuhan²,Elliott Hunter¹,Glass Benjamin¹,Pethia Kishalve¹,Carrasco‐Zevallos Oscar¹,Shukla Chinmay¹,Khettry Urmila³,Najarian Robert⁴,Taliano Ross⁵,Subramanian G. Mani²,Myers Robert P.²,Wapinski Ilan¹,Khosla Aditya¹,Resnick Murray¹⁵,Montalto Michael C.¹,Anstee Quentin M.⁶^ORCID,Wong Vincent Wai‐Sun⁷^ORCID,Trauner Michael⁸,Lawitz Eric J.⁹,Harrison Stephen A.¹⁰,Okanoue Takeshi¹¹,Romero‐Gomez Manuel¹²^ORCID,Goodman Zachary¹³¹⁴,Loomba Rohit¹⁵,Beck Andrew H.¹^ORCID,Younossi Zobair M.¹³¹⁴

Affiliation:

1. PathAI Boston MA

2. Gilead Sciences, Inc. Foster City CA

3. Lahey Hospital & Medical Center (Emeritus) Burlington MA

4. University Gastroenterology Portsmouth RI

5. Warren Alpert Medical School of Brown University Providence RI

6. Translational & Clinical Research Institute, Faculty of Medical Sciences Newcastle University Newcastle upon Tyne UK

7. Department of Medicine and Therapeutics The Chinese University of Hong Kong Hong Kong Hong Kong

8. Division of Gastroenterology and Hepatology Medical University of Vienna Vienna Austria

9. Texas Liver Institute UT Health San Antonio San Antonio TX

10. Pinnacle Clinical Research San Antonio TX

11. Saiseikai Suita Hospital Suita City Japan