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 presentatio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Annals of Hepatology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1665268123003812 |
| _version_ | 1797281187592404992 |
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| author | Maria Jimenez Ramos Timothy J. Kendall Ignat Drozdov Jonathan A. Fallowfield |
| author_facet | Maria Jimenez Ramos Timothy J. Kendall Ignat Drozdov Jonathan A. Fallowfield |
| author_sort | Maria Jimenez Ramos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T16:53:52Z |
| format | Article |
| id | doaj.art-59adc7da25d94392a91b1e59fc8d46cb |
| institution | Directory Open Access Journal |
| issn | 1665-2681 |
| language | English |
| last_indexed | 2024-03-07T16:53:52Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Annals of Hepatology |
| spelling | doaj.art-59adc7da25d94392a91b1e59fc8d46cb2024-03-03T04:29:05ZengElsevierAnnals of Hepatology1665-26812024-03-01292101278A data-driven approach to decode metabolic dysfunction-associated steatotic liver diseaseMaria Jimenez Ramos0Timothy J. Kendall1Ignat Drozdov2Jonathan A. Fallowfield3Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UKCentre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK; Edinburgh Pathology, University of Edinburgh, 51 Little France Crescent, Old Dalkeith Rd, Edinburgh EH16 4SA, UKBering Limited, 54 Portland Place, London, W1B 1DY, UKCentre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S1665268123003812NAFLDMASLDBig dataArtificial intelligenceMachine LearningPrecision medicine |
| spellingShingle | Maria Jimenez Ramos Timothy J. Kendall Ignat Drozdov Jonathan A. Fallowfield A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease Annals of Hepatology NAFLD MASLD Big data Artificial intelligence Machine Learning Precision medicine |
| title | A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease |
| title_full | A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease |
| title_fullStr | A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease |
| title_full_unstemmed | A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease |
| title_short | A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease |
| title_sort | data driven approach to decode metabolic dysfunction associated steatotic liver disease |
| topic | NAFLD MASLD Big data Artificial intelligence Machine Learning Precision medicine |
| url | http://www.sciencedirect.com/science/article/pii/S1665268123003812 |
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