TY  - JOUR
AU  - Taylor-Weiner, Amaro
AU  - Pokkalla, Harsha
AU  - Han, Ling
AU  - Jia, Catherine
AU  - Huss, Ryan
AU  - Chung, Chuhan
AU  - Elliott, Hunter
AU  - Glass, Benjamin
AU  - Pethia, Kishalve
AU  - Carrasco-Zevallos, Oscar
AU  - Shukla, Chinmay
AU  - Khettry, Urmila
AU  - Najarian, Robert
AU  - Taliano, Ross
AU  - Subramanian, G Mani
AU  - Myers, Robert P
AU  - Wapinski, Ilan
AU  - Khosla, Aditya
AU  - Resnick, Murray
AU  - Montalto, Michael C
AU  - Anstee, Quentin M
AU  - Wong, Vincent Wai-Sun
AU  - Trauner, Michael
AU  - Lawitz, Eric J
AU  - Harrison, Stephen A
AU  - Okanoue, Takeshi
AU  - Romero-Gomez, Manuel
AU  - Goodman, Zachary
AU  - Loomba, Rohit
AU  - Beck, Andrew H
AU  - Younossi, Zobair M
PY  - 2021
DO  - 10.1002/hep.31750
UR  - http://hdl.handle.net/10668/17146
T2  - Hepatology (Baltimore, Md.)
AB  - Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools...
LA  - en
KW  - Biopsy
KW  - Deep Learning
KW  - Humans
KW  - Image Processing, Computer-Assisted
KW  - Liver
KW  - Liver Cirrhosis
KW  - Non-alcoholic Fatty Liver Disease
KW  - Randomized Controlled Trials as Topic
KW  - Reproducibility of Results
KW  - Severity of Illness Index
TI  - A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.
TY  - research article
VL  - 74
ER  -