AI project


Full title: The LiverTRAIL software for early detection of severe liver fibrosis in patients with alcoholic- and non-alcoholic fatty liver disease


This Ph.D. project will develop and validate a decision aid for use in primary health care to assess the risk of advanced fibrosis in patients with alcoholic- and non-alcoholic fatty liver disease. The decision aid – LiverTRAIL – works algorithm-based, by combining results from routine liver blood tests into multiple diagnostic algorithms, thus utilising computational power for large-scale data aggregation and pattern recognition.

Our aim is that LiverTRAIL can transform knowledge from original research data into a rational decision tool for the general practitioner. 

LiverTRAIL will unite three main challenges within the study of fatty liver disease:

  1. Identify patients with severe fibrosis due to ALD and NAFLD (rule-in).
  2. Exclude patients with ALD and NAFLD at very low risk of advanced fibrosis from further diagnostic investigations (rule-out).

Monitor patients at high- and moderate risk of advanced fibrosis for disease progression and fibrosis improvement, e.g. during anti-fibrotic treatment.We hypothesize that through the implementation of LiverTRAIL in the primary sector, it is possible to achieve early detection of patients with asymptomatic, early-stage severe fibrosis and cirrhosis.


FLASH – Center for Liver Research, OUH

Professor Esmaeil S. Nadimi, Applied AI and Data Science, The Maersk Mc-Kinney Moller Institute, Faculty of Engineering, SDU


The project is funded by Innovation Fund Denmark.

Project period

Start: December 2018

End: November 2022

Read more

Read more about the first results from the project in the article Artificial intelligence outperforms standard blood-based scores in identifying liver fibrosis patients in primary care. The article was published in Scientific Reports, part of the Nature Portfolio.


Katrine Prier Lindvig headshot

Katrine Prier Lindvig

PhD student, MD

FLASH – Center for Liver Research

(+45) 28 83 87 54
[email protected]