| Literature DB >> 34396467 |
Yifang Li1, Xuetao Wang1, Jun Zhang1, Shanshan Zhang1, Jian Jiao2.
Abstract
Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.Entities:
Keywords: Artificial intelligence (AI); Machine learning (ML); Metabolic associated fatty liver disease (MAFLD); Non-alcoholic fatty liver disease (NAFLD); Non-alcoholic steatohepatitis (NASH)
Mesh:
Year: 2021 PMID: 34396467 DOI: 10.1007/s11154-021-09681-x
Source DB: PubMed Journal: Rev Endocr Metab Disord ISSN: 1389-9155 Impact factor: 9.306