Machine learning approaches using blood biomarkers in nonalcoholic fatty liver diseases

作     者：Randhall B Carteri Mateus Grellert Daniela Luisa Borba Claudio Augusto Marroni Sabrina Alves Fernandes 

作者机构：Department of NutritionMethodist University Center-IPAPorto Alegre 90420-060Rio Grande do SulBrazil Department of Health and BehaviourCatholic University of PelotasPelotas 96015-560Rio Grande do SulBrazil Department of Informatics and StatisticsFederal University of Santa CatarinaFlorianópolis 88040-900Santa CatarinaBrazil Postgraduate Program in HepatologyFederal University of Health Sciences of Porto AlegrePorto Alegre 90050-170Rio Grande do SulBrazil Department of Gastroenterology and HepatologyFederal University of Health Sciences of Porto AlegrePorto Alegre 90050-170Rio Grande do SulBrazil 

出 版 物：《Artificial Intelligence in Gastroenterology》 (胃肠病学中的人工智能（英文）)

年 卷 期：2022年第3卷第3期

页      面：80-87页

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

主　　题：Artificial intelligence Liver diseases Healthcare Hepatology Prognosis Diagnostics 

摘      要：The prevalence of nonalcoholic fatty liver disease(NAFLD)is an important public health *** diagnosis of NAFLD and potential progression to nonalcoholic steatohepatitis(NASH),could reduce the further advance of the disease,and improve patient *** 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 lessinvasive *** 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 ***,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 *** 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.