AN international team including researchers from the University of Edinburgh has been awarded €750,000 (£690,000) to develop artificial intelligence (AI) diagnostic tools for liver disease.
The project will look at ways of interpreting patient data to predict when non-alcoholic fatty liver disease (NAFLD) is likely to progress into dangerous conditions such as cirrhosis of the liver and liver cancer.
Professor Jonathan Fallowfield and Dr Tim Kendall, from Edinburgh’s Centre for Inflammation Research, will work with a specialist imaging company based in Singapore and two British firms specialising in data management and AI techniques.
Around a quarter of people have NAFLD, which affects millions of people worldwide and which is often unnoticed and undiagnosed. There is no way of telling which people with the disease might develop the severe form or progress to cirrhosis.
Funding for the project, named INTErPRET-NAFLD, has come from UK Research and Innovation, and support from Edinburgh Innovations, the university’s commercialisation arm.
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Fallowfield said: “Identifying the high-risk NAFLD population – those who progress to adverse clinical outcomes – remains the holy grail in liver disease. In this project we will work with experts in digital pathology, data science and AI to investigate a very large dataset, incorporating existing real-world patient data derived from liver biopsies, genomics and electronic medical records.
“Our ambition is to develop risk prediction models that can be embedded into routine clinical care and facilitate a ‘precision health’ approach to NAFLD.”
HistoIndex in Singapore will carry out AI-augmented image analysis of liver biopsies from a Scottish database.
Glasgow-based Biodev will process the enriched dataset, and Bering in London, which develops medical applications of AI, will process them.
Bering’s MD, Dr Ignat Drozdov, said: “By bringing together experts across clinical and technological domains, we are developing a truly personalised precision-medicine approach to this global problem.”
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