TY - JOUR
T1 - Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis
T2 - current status and future directions
AU - Ratziu, Vlad
AU - Hompesch, Marcus
AU - Petitjean, Mathieu
AU - Serdjebi, Cindy
AU - Iyer, Janani S.
AU - Parwani, Anil V.
AU - Tai, Dean
AU - Bugianesi, Elisabetta
AU - Cusi, Kenneth
AU - Friedman, Scott L.
AU - Lawitz, Eric
AU - Romero-Gómez, Manuel
AU - Schuppan, Detlef
AU - Loomba, Rohit
AU - Paradis, Valérie
AU - Behling, Cynthia
AU - Sanyal, Arun J.
N1 - Publisher Copyright:
© 2023 European Association for the Study of the Liver
PY - 2024/2
Y1 - 2024/2
N2 - The worldwide prevalence of non-alcoholic steatohepatitis (NASH) is increasing, causing a significant medical burden, but no approved therapeutics are currently available. NASH drug development requires histological analysis of liver biopsies by expert pathologists for trial enrolment and efficacy assessment, which can be hindered by multiple issues including sample heterogeneity, inter-reader and intra-reader variability, and ordinal scoring systems. Consequently, there is a high unmet need for accurate, reproducible, quantitative, and automated methods to assist pathologists with histological analysis to improve the precision around treatment and efficacy assessment. Digital pathology (DP) workflows in combination with artificial intelligence (AI) have been established in other areas of medicine and are being actively investigated in NASH to assist pathologists in the evaluation and scoring of NASH histology. DP/AI models can be used to automatically detect, localise, quantify, and score histological parameters and have the potential to reduce the impact of scoring variability in NASH clinical trials. This narrative review provides an overview of DP/AI tools in development for NASH, highlights key regulatory considerations, and discusses how these advances may impact the future of NASH clinical management and drug development. This should be a high priority in the NASH field, particularly to improve the development of safe and effective therapeutics.
AB - The worldwide prevalence of non-alcoholic steatohepatitis (NASH) is increasing, causing a significant medical burden, but no approved therapeutics are currently available. NASH drug development requires histological analysis of liver biopsies by expert pathologists for trial enrolment and efficacy assessment, which can be hindered by multiple issues including sample heterogeneity, inter-reader and intra-reader variability, and ordinal scoring systems. Consequently, there is a high unmet need for accurate, reproducible, quantitative, and automated methods to assist pathologists with histological analysis to improve the precision around treatment and efficacy assessment. Digital pathology (DP) workflows in combination with artificial intelligence (AI) have been established in other areas of medicine and are being actively investigated in NASH to assist pathologists in the evaluation and scoring of NASH histology. DP/AI models can be used to automatically detect, localise, quantify, and score histological parameters and have the potential to reduce the impact of scoring variability in NASH clinical trials. This narrative review provides an overview of DP/AI tools in development for NASH, highlights key regulatory considerations, and discusses how these advances may impact the future of NASH clinical management and drug development. This should be a high priority in the NASH field, particularly to improve the development of safe and effective therapeutics.
KW - NAFLD
KW - NASH
KW - artificial intelligence
KW - ballooning
KW - clinical trials
KW - digital pathology
KW - fibrosis
KW - histology
KW - inflammation
KW - liver biopsy
KW - machine learning
KW - steatosis
UR - http://www.scopus.com/inward/record.url?scp=85180571217&partnerID=8YFLogxK
U2 - 10.1016/j.jhep.2023.10.015
DO - 10.1016/j.jhep.2023.10.015
M3 - Review article
C2 - 37879461
AN - SCOPUS:85180571217
SN - 0168-8278
VL - 80
SP - 335
EP - 351
JO - Journal of Hepatology
JF - Journal of Hepatology
IS - 2
ER -