FibrAIm - The machine learning approach to identify the early stage of liver fibrosis and steatosis.

Journal: International journal of medical informatics
PMID: 39983467

Abstract

BACKGROUND: Early recognition of steatosis (fatty liver) and fibrosis in liver health is crucial for effectively managing and preventing the possibility of liver dysfunction. Detecting steatosis helps identify individuals at risk of liver-related diseases, such as inflammation (Non-Alcoholic SteatoHepatitis, NASH) and fibrosis. Fibrosis involves the formation of scar tissue in the liver due to chronic inflammation and injury. Early recognition of fibrosis helps categorize patients based on their risk of progression to advanced liver disease. Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) leads to many outcomes, including Metabolic dysfunction-Associated Steatohepatitis (MASH), fibrosis, and cirrhosis. We aim to show that routine clinical tests supported by machine learning offer sufficient information to predict these endpoints.

Authors

  • Barbara Ginter-Matuszewska
    Department of Infectious Diseases, Hepatology and Acquired Immunodeficiencies, Poznan University of Medical Sciences, 3 Szwajcarska Street, 61-285 Poznan, Poland. Electronic address: bgintermatuszewska@ump.edu.pl.
  • Agnieszka Adamek
    Department of Infectious Diseases, Hepatology and Acquired Immunodeficiencies, Poznan University of Medical Sciences, 3 Szwajcarska Street, 61-285 Poznan, Poland.
  • Maciej Majchrzak
    Institute of Computing Sciences, Faculty of Computing and Telecommunications, Poznan University of Technology, 2 Piotrowo Street, 60-965 Poznan, Poland.
  • Blazej Rozplochowski
    Department of Infectious Diseases, Hepatology and Acquired Immunodeficiencies, Poznan University of Medical Sciences, 3 Szwajcarska Street, 61-285 Poznan, Poland.
  • Agata Zientarska
    Clinical Department of Paediatrics and Infectious Diseases, Wroclaw Medical University, 2-2a Chalubinskiego Street, 50-368 Wroclaw, Poland.
  • Arleta Kowala-Piaskowska
    Department of Infectious Diseases, Hepatology and Acquired Immunodeficiencies, Poznan University of Medical Sciences, 3 Szwajcarska Street, 61-285 Poznan, Poland.
  • Piotr Lukasiak
    Institute of Computing Sciences, Faculty of Computing and Telecommunications, Poznan University of Technology, 2 Piotrowo Street, 60-965 Poznan, Poland. Electronic address: Piotr.Lukasiak@put.poznan.pl.

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