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

BACKGROUND AND AIMS: 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 to assess liver pathology i...

Descripción completa

Detalles Bibliográficos
Autores principales: Taylor‐Weiner, Amaro, Pokkalla, Harsha, Han, Ling, Jia, Catherine, Huss, Ryan, Chung, Chuhan, Elliott, Hunter, Glass, Benjamin, Pethia, Kishalve, Carrasco‐Zevallos, Oscar, Shukla, Chinmay, Khettry, Urmila, Najarian, Robert, Taliano, Ross, Subramanian, G. Mani, Myers, Robert P., Wapinski, Ilan, Khosla, Aditya, Resnick, Murray, Montalto, Michael C., Anstee, Quentin M., Wong, Vincent Wai‐Sun, Trauner, Michael, Lawitz, Eric J., Harrison, Stephen A., Okanoue, Takeshi, Romero‐Gomez, Manuel, Goodman, Zachary, Loomba, Rohit, Beck, Andrew H., Younossi, Zobair M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361999/
https://www.ncbi.nlm.nih.gov/pubmed/33570776
http://dx.doi.org/10.1002/hep.31750

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361999/
https://www.ncbi.nlm.nih.gov/pubmed/33570776
http://dx.doi.org/10.1002/hep.31750

Ejemplares similares