Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY.
Liver Institute Northwest, Seattle, WA; Elson S. Floyd College of Medicine, Washington State University, WA.
Hepatology. 2021 Oct;74(4):2233-2240. doi: 10.1002/hep.31869. Epub 2021 Aug 10.
The diagnosis of nonalcoholic fatty liver disease and associated fibrosis is challenging given the lack of signs, symptoms and nonexistent diagnostic test. Furthermore, follow up and treatment decisions become complicated with a lack of a simple reproducible method to follow these patients longitudinally. Liver biopsy is the current standard to detect, risk stratify and monitor individuals with nonalcoholic fatty liver disease. However, this method is an unrealistic option in a population that affects about one in three to four individuals worldwide. There is an urgency to develop innovative methods to facilitate management at key points in an individual's journey with nonalcoholic fatty liver disease fibrosis. Artificial intelligence is an exciting field that has the potential to achieve this. In this review, we highlight applications of artificial intelligence by leveraging our current knowledge of nonalcoholic fatty liver disease to diagnose and risk stratify NASH phenotypes.
鉴于非酒精性脂肪性肝病缺乏体征、症状和不存在诊断性检测，因此其诊断具有挑战性。此外，由于缺乏一种简单、可重复的方法对这些患者进行纵向随访，因此随访和治疗决策变得复杂。肝活检是目前用于检测、风险分层和监测非酒精性脂肪性肝病患者的标准方法。然而，在全球每三到四个人中就有一人受到影响的人群中，这种方法不切实际。因此，迫切需要开发创新方法，以促进在非酒精性脂肪性肝病纤维化患者的各个关键阶段的管理。人工智能是一个令人兴奋的领域，具有实现这一目标的潜力。在这篇综述中，我们通过利用我们目前对非酒精性脂肪性肝病的了解，强调了人工智能在诊断和风险分层 NASH 表型方面的应用。
