A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease.

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. The use of 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 the context of 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.

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Copyright © 2023. Published by Elsevier España, S.L.U.

Déclaration de conflit d'intérêts

Conflicts of interest T.J.K. serves as a consultant for or has received speakers’ fees from Resolution Therapeutics, Clinnovate Health, Perspectum, Servier Laboratories, Kynos Therapeutics, and Incyte Corporation. J.A.F. serves as a consultant or advisory board member for Resolution Therapeutics, Kynos Therapeutics, Sosei Heptares, Ipsen, Redx Pharma, River 2 Renal Corp., Stimuliver, Galecto Biotech, Global Clinical Trial Partners and Guidepoint and has received research grant funding from Intercept Pharmaceuticals and Genentech. I.D. is a shareholder in Bering Limited.