A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease.
Ann Hepatol
; 29(2): 101278, 2024.
Article
em En
| MEDLINE
| ID: mdl-38135251
ABSTRACT
Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fígado Gorduroso
/
Doenças Metabólicas
Limite:
Humans
Idioma:
En
Revista:
Ann Hepatol
Assunto da revista:
GASTROENTEROLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de publicação:
México