The NHS in England is launching a world-first trial of a “gamechanging” artificial intelligence tool that can identify patients at risk of type 2 diabetes more than a decade before they develop the condition,El.kz cites the Guardian.
More than 500 million people worldwide have type 2 diabetes, and finding new ways to spot people at risk before they develop the condition is a major global health priority. Estimates suggest 1 billion people will have type 2 diabetes by 2050.
The condition is a leading cause of blindness, kidney failure, heart attacks, strokes and lower limb amputation. It is often linked to being overweight or inactive, or having a family history of type 2 diabetes, although not all those diagnosed are in those categories.
Now doctors and scientists have developed a transformative AI tool that can predict those at risk of the condition as much as 13 years before it begins to develop.
The technology analyses electrocardiogram (ECG) readings during routine heart scans. It can detect subtle changes too small to be noticed by the human eye that could raise the alarm early about a patient on the road to getting type 2 diabetes.
The NHS will begin trialling the tool in 2025 at Imperial College healthcare NHS trust and Chelsea and Westminster hospital NHS foundation trust, and will be the first healthcare system in the world to do so, the Guardian has learned.
Those involved in developing the technology, called the AI-ECG risk rstimation for diabetes mellitus (Aire-DM), hope it could be rolled out across the health service in England and in other countries within the next few years.
“AI holds enormous potential to transform care that could lead to substantial improvements in health,” said Dr Libor Pastika, a clinical research training fellow at Imperial. “By using AI to unlock insights hidden within ECG data, Aire-DM could be revolutionary in identifying future risk of type 2 diabetes early on.
“Offering a cheap, accessible, non-invasive way to predict type 2 diabetes risk early, Aire-DM could open up a new window of opportunity for more targeted, preventive care, helping people avoid the condition and its associated complications.”
A team led by Dr Fu Siong Ng and Dr Arunashis Sau at Imperial developed the tool using 1.2m ECGs from hospital records. They then used data from the UK Biobank, which holds the genetic data and medical records of more than 500,000 participants, to validate the tool’s ability to detect subtle changes in ECGs.
The tool maps tiny ECG patterns more common in those who will go on to develop type 2 diabetes in the future – and then looks for those same patterns in new ECGs.
The telltale signs include variations in how the heart’s electrical signal travels, such as small changes in the timing, shape, or patterns of certain electrical waves.