Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis

Grace Lai Hung Wong*, Pong Chi YUEN, Andy Jinhua Ma, Anthony Wing Hung Chan, Howard Ho Wai Leung, Vincent Wai Sun Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)543-550
Number of pages8
JournalJournal of Gastroenterology and Hepatology (Australia)
Volume36
Issue number3
DOIs
Publication statusPublished - Mar 2021

Scopus Subject Areas

  • Hepatology
  • Gastroenterology

User-Defined Keywords

  • Cirrhosis
  • Liver fibrosis
  • Machine learning
  • Non-alcoholic steatohepatitis (NASH)

Fingerprint

Dive into the research topics of 'Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis'. Together they form a unique fingerprint.

Cite this