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Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosis
Author(s) -
Wong Grace LaiHung,
Yuen PongChi,
Ma Andy Jinhua,
Chan Anthony WingHung,
Leung Howard HoWai,
Wong Vincent WaiSun
Publication year - 2021
Publication title -
journal of gastroenterology and hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.214
H-Index - 130
eISSN - 1440-1746
pISSN - 0815-9319
DOI - 10.1111/jgh.15385
Subject(s) - medicine , fatty liver , transient elastography , liver biopsy , decision tree , random forest , artificial neural network , disease , liver disease , elastography , artificial intelligence , biopsy , pathology , radiology , computer science , ultrasound
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.