Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence

Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence

BackgroundChildhood obesity is a growing problem worldwide, leading to non-alcoholic fatty liver disease (NAFLD), which is the most common liver disease in children. Liver biopsy is the gold standard for NAFLD diagnosis. Machine learning algorithms could assist in an early diagnostic approach and le...

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Main Authors: Aliakbar Sayyari, Amin Magsudy, Yasamin Moeinipour, Amirhossein Hosseini, Hamidreza Amiri, Mohammadreza Arzaghi, Fereshteh Sohrabivafa, Seyedeh Fatemeh Hamzavi, Ashkan Azizi, Tahereh Hatamii, AmirAli Okhovat, Naghi Dara, Negar Imanzadeh, Farid Imanzadeh, Mahmoud Hajipour
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Pediatrics
Subjects:
NAFLD
machine learning
predictive factors
childhood obesity
fibrosis
CatBoost
Online Access:https://www.frontiersin.org/articles/10.3389/fped.2025.1537098/full
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https://www.frontiersin.org/articles/10.3389/fped.2025.1537098/full

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