Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong.
Medical Data Analytic Centre (MDAC), The Chinese University of Hong Kong, Shatin, Hong Kong.
J Gastroenterol Hepatol. 2021 Mar;36(3):543-550. doi: 10.1111/jgh.15385.
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.
人工智能（AI）在我们的日常生活中已经越来越普及，包括在医疗保健领域的应用。AI 为我们提供了许多新的思路，可以更好地治疗慢性肝病患者，包括非酒精性脂肪性肝病和肝纤维化。在传统的侵入性（肝活检）和非侵入性（瞬时弹性成像、血清标志物或临床预测模型）方法的基础上，有多种方法可以应用 AI 技术。在这篇综述文章中，我们讨论了在电子病历、肝活检和肝图像中应用 AI 的原理。一些常见的 AI 方法包括在单个时间点使用逻辑回归、决策树、随机森林和 XGBoost，使用递归神经网络处理序列数据，以及使用深度神经网络处理组织学和图像。
