Carteri RB, Grellert M, Borba DL, Marroni CA, Fernandes SA. Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases. Artif Intell Gastroenterol 2022; 3(3): 80-87 [DOI: 10.35712/aig.v3.i3.80]
Corresponding Author of This Article
Sabrina Alves Fernandes, PhD, Research Scientist, Teacher, Postgraduate Program in Hepatology, Federal University of Health Sciences of Porto Alegre, Sarmento Leite Street, 245 - Centro Histórico, Porto Alegre 90050-170, Rio Grande do Sul, Brazil. sabrinaafernandes@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Gastroenterol. Jun 28, 2022; 3(3): 80-87 Published online Jun 28, 2022. doi: 10.35712/aig.v3.i3.80
Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases
Randhall B Carteri, Mateus Grellert, Daniela Luisa Borba, Claudio Augusto Marroni, Sabrina Alves Fernandes
Randhall B Carteri, Department of Nutrition, Methodist University Center - IPA, Porto Alegre 90420-060, Rio Grande do Sul, Brazil
Randhall B Carteri, Department of Health and Behaviour, Catholic University of Pelotas, Pelotas 96015-560, Rio Grande do Sul, Brazil
Mateus Grellert, Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis 88040-900, Santa Catarina, Brazil
Daniela Luisa Borba, Sabrina Alves Fernandes, Postgraduate Program in Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Rio Grande do Sul, Brazil
Claudio Augusto Marroni, Department of Gastroenterology and Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Rio Grande do Sul, Brazil
Author contributions: Carteri RB organized the structuring of the article in the order of the subheadings covered; Grellert M and Borba DL contributed to manuscript writing; Maroni CA and Fernandes SA supervised and revised the manuscript; All authors contributed to the writing of the article and review of the scientific article.
Conflict-of-interest statement: All authors declare that there are no conflicts of interest related to this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Sabrina Alves Fernandes, PhD, Research Scientist, Teacher, Postgraduate Program in Hepatology, Federal University of Health Sciences of Porto Alegre, Sarmento Leite Street, 245 - Centro Histórico, Porto Alegre 90050-170, Rio Grande do Sul, Brazil. sabrinaafernandes@gmail.com
Received: December 31, 2021 Peer-review started: December 31, 2021 First decision: March 28, 2022 Revised: April 15, 2022 Accepted: May 8, 2022 Article in press: May 8, 2022 Published online: June 28, 2022 Processing time: 179 Days and 7.3 Hours
Abstract
The prevalence of nonalcoholic fatty liver disease (NAFLD) is an important public health concern. Early diagnosis of NAFLD and potential progression to nonalcoholic steatohepatitis (NASH), could reduce the further advance of the disease, and improve patient outcomes. Aiming to support patient diagnostic and predict specific outcomes, the interest in artificial intelligence (AI) methods in hepatology has dramatically increased, especially with the application of less-invasive biomarkers. In this review, our objective was twofold: Firstly, we presented the most frequent blood biomarkers in NAFLD and NASH and secondly, we reviewed recent literature regarding the use of machine learning (ML) methods to predict NAFLD and NASH in large cohorts. Strikingly, these studies provide insights into ML application in NAFLD patients' prognostics and ranked blood biomarkers are able to provide a recognizable signature allowing cost-effective NAFLD prediction and also differentiating NASH patients. Future studies should consider the limitations in the current literature and expand the application of these algorithms in different populations, fortifying an already promising tool in medical science.
Core Tip: The ability of machine learning approaches to process multiple variables, map linear and nonlinear interactions, ranking the most important features, in addition to the capability of building accurate prediction models, sets a future direction to its application in complex diseases such as nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Future studies should consider the limitations in the current literature and expand the application of these algorithms in different populations, fortifying an already promising tool in medical science.
