Literature DB >> 33709610

Artificial intelligence in the diagnosis and management of hepatocellular carcinoma.

Masaya Sato1,2, Ryosuke Tateishi2, Yutaka Yatomi1, Kazuhiko Koike2.   

Abstract

Despite recent improvements in therapeutic interventions, hepatocellular carcinoma is still associated with a poor prognosis in patients with an advanced disease at diagnosis. Recently, significant progress has been made in image recognition through advances in the field of artificial intelligence (AI) (or machine learning), especially deep learning. AI is a multidisciplinary field that draws on the fields of computer science and mathematics for developing and implementing computer algorithms capable of maximizing the predictive accuracy from static or dynamic data sources using analytic or probabilistic models. Because of the multifactorial and complex nature of liver diseases, the machine learning approach to integrate multiple factors would appear to be an advantageous approach to improve the likelihood of making a precise diagnosis and predicting the response of treatment and prognosis of liver diseases. In this review, we attempted to summarize the potential use of AI in the diagnosis and management of liver diseases, especially hepatocellular carcinoma.
© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Entities:  

Keywords:  artificial intelligence; deep learning; hepatocellular carcinoma; machine learning; predictive model

Year:  2021        PMID: 33709610     DOI: 10.1111/jgh.15413

Source DB:  PubMed          Journal:  J Gastroenterol Hepatol        ISSN: 0815-9319            Impact factor:   4.029


  3 in total

1.  A Novel Prognostic Score Based on Artificial Intelligence in Hepatocellular Carcinoma: A Long-Term Follow-Up Analysis.

Authors:  Xiaoli Liu; Xinhui Wang; Lihua Yu; Yixin Hou; Yuyong Jiang; Xianbo Wang; Junyan Han; Zhiyun Yang
Journal:  Front Oncol       Date:  2022-05-31       Impact factor: 5.738

2.  Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research.

Authors:  Yuan Wang; Chao Wei; Xiangui Deng; Shudi Gao; Jing Chen
Journal:  Biomed Res Int       Date:  2022-09-19       Impact factor: 3.246

3.  Long non-coding RNA LOC107985656 represses the proliferation of hepatocellular carcinoma cells through activation of the tumor-suppressive Hippo pathway.

Authors:  Yu Zeng; Qin Xu; Nan Xu
Journal:  Bioengineered       Date:  2021-12       Impact factor: 3.269

  3 in total

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