Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis.
Grace Lai-Hung WongPong-Chi YuenAndy Jinhua MaAnthony Wing-Hung ChanHoward Ho-Wai LeungVincent Wai-Sun WongPublished in: Journal of gastroenterology and hepatology (2021)
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.
Keyphrases
- artificial intelligence
- liver fibrosis
- neural network
- electronic health record
- deep learning
- big data
- machine learning
- healthcare
- convolutional neural network
- clinical decision support
- ultrasound guided
- optical coherence tomography
- climate change
- adverse drug
- fine needle aspiration
- quality improvement
- physical activity
- blood brain barrier
- pain management
- cerebral ischemia