Artificial intelligence ‘could help identify future diabetes patients’

Scientists have developed a new technology that uses artificial intelligence to predict whether a patient will develop diabetes.

The preliminary research, which is yet to be published, applies a method known as machine learning to assess the risk of a person developing the lifelong condition that causes high blood sugar levels.

Dr Akihiro Nomura, of the Kanazawa University Graduate School of Medical Sciences in Kanazawa, Japan, and one of the study authors, said: “Currently we do not have sufficient methods for predicting which generally healthy individuals will develop diabetes.”

Around 3.9 million in the UK are living with diabetes, according to charity Diabetes UK.

The condition has been linked to increased risks of other health problems, including heart disease and cancer.

The researchers investigated whether machine learning could be used to diagnose diabetes.

They used health records from more than 139,000 participants in Kanazawa, which included around 74,000 diabetes patients.

Physical exams, blood and urine tests were part of the data from the medical records.

The team also made note of new cases of diabetes recorded during the patients’ annual health checks.

Dr Nomura and his colleagues then used the data to train a machine-learning algorithm to predict those at the risk of developing diabetes in the future.

The researchers said their algorithm had an overall accuracy of 94.9%, and was able to identify more than 4,000 new diabetes patients.

Dr Nomura said: “Using machine learning, it could be possible to precisely identify high-risk groups of future diabetes patients better than using existing risk scores.

“In addition, the rate of visits to healthcare providers might be improved to prevent future onset of diabetes.”

The findings were due to be presented at the Endocrine Society’s annual meeting, which was cancelled due to the Covid-19 outbreak.

The research will be published in the Journal of the Endocrine Society in mid-April.

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