Scottish scientists using AI to analyse medical data have been able to predict a person’s probability of developing conditions such as heart disease, type 2 diabetes, Alzheimer’s up to 10 years before a diagnosis.

The researchers have used machine learning to study blood samples from more than 45,000 people—with the samples taken from the UK Biobank, a database of genetic and health information from 500,000 British participants.

The study team, which involved researchers from the University of Edinburgh and commercial collaborators Optima Partners and Biogen, used the tech to identify protein patterns in the blood that were indicative of the development of the aforementioned conditions.

The team then tested whether the patterns could be used to diagnose conditions in the blood samples of a separate group of individuals, whose data had not been used to create the protein patterns.

They found that the protein patterns improved prediction accuracy beyond traditional risk factors such as age, sex, lifestyle behaviours, cholesterol, and other commonly-measured clinical variables.

Being able to detect early warning signs for a broad set of conditions may lead to opportunities for early intervention and prevention, experts say.

Implementation of this form of analysis is not expected to be immediate, but it’s been said that their research is a promising step forwards in health risk prediction.

Dr Danni Gadd, a PhD student who’s a part of the Riccardo Marioni Research Group at the University of Edinburgh, said: “It’s encouraging to see how much potential there is from a single blood sample that allow us to predict a range of disease outcomes.

“Being able to detect early warning signs for a broad set of conditions may lead to opportunities for early intervention and prevention, marking a significant moment for the healthcare industry.”

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Dr Chris Foley, managing director and chief scientist at Optima Partners, added: “More work is still needed to convert these findings for practical use in clinical settings. However, our discoveries set strong foundations for the inclusion of new risk prediction signatures to shed light on possible pathways and mechanisms that underlie diseases.

“Pattern recognition like this would not be possible without modern machine learning technology, and its capacity to analyse data at this scale, and this will in turn allow us to address some of the most pressing healthcare challenges of our time.”