TY - JOUR
T1 - Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis
AU - Wong, Grace Lai Hung
AU - Yuen, Pong Chi
AU - Ma, Andy Jinhua
AU - Chan, Anthony Wing Hung
AU - Leung, Howard Ho Wai
AU - Wong, Vincent Wai Sun
N1 - Funding Information:
This study was funded in part by the Health and Medical Research Fund (HMRF) of Food and Health Bureau, Hong Kong (project reference number: 07180216) to Grace Lai‐Hung Wong. Financial support:
© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.
PY - 2021/3/11
Y1 - 2021/3/11
N2 - Artificial intelligence (AI) has become increasingly widespread in our daily lives, including healthcare applications. AI has brought many new insights into better ways we care for our patients with chronic liver disease, including non-alcoholic fatty liver disease and liver fibrosis. There are multiple ways to apply the AI technology on top of the conventional invasive (liver biopsy) and noninvasive (transient elastography, serum biomarkers, or clinical prediction models) approaches. In this review article, we discuss the principles of applying AI on electronic health records, liver biopsy, and liver images. A few common AI approaches include logistic regression, decision tree, random forest, and XGBoost for data at a single time stamp, recurrent neural networks for sequential data, and deep neural networks for histology and images.
AB - Artificial intelligence (AI) has become increasingly widespread in our daily lives, including healthcare applications. AI has brought many new insights into better ways we care for our patients with chronic liver disease, including non-alcoholic fatty liver disease and liver fibrosis. There are multiple ways to apply the AI technology on top of the conventional invasive (liver biopsy) and noninvasive (transient elastography, serum biomarkers, or clinical prediction models) approaches. In this review article, we discuss the principles of applying AI on electronic health records, liver biopsy, and liver images. A few common AI approaches include logistic regression, decision tree, random forest, and XGBoost for data at a single time stamp, recurrent neural networks for sequential data, and deep neural networks for histology and images.
KW - Cirrhosis
KW - Liver fibrosis
KW - Machine learning
KW - Non-alcoholic steatohepatitis (NASH)
UR - http://www.scopus.com/inward/record.url?scp=85102510897&partnerID=8YFLogxK
U2 - 10.1111/jgh.15385
DO - 10.1111/jgh.15385
M3 - Journal article
C2 - 33709607
AN - SCOPUS:85102510897
SN - 0815-9319
VL - 36
SP - 543
EP - 550
JO - Journal of Gastroenterology and Hepatology
JF - Journal of Gastroenterology and Hepatology
IS - 3
ER -