Researchers tout AI-based diabetes onset prediction method

Scientists at Klick Applied Sciences say they discovered a way to use a continuous glucose monitor (CGM) as a diabetes screening tool.

The scientists determined that they can use artificial intelligence (AI) to screen and prevent diabetes. They used machine learning and 12 hours of data from CGMs to determine whether a patient had prediabetes or diabetes.

“We have demonstrated that 12 hours of monitoring can make a big difference in the lives of people at risk of developing diabetes while there’s still time to course correct,” said Jouhyun Jeon, lead scientist of the study and principal investigator at Klick Applied Sciences. “We think CGMs could be used to not just monitor diabetes–but to prevent it altogether.”

About the diabetes study

The study evaluated 600 patients. They identified as healthy, prediabetic or living with type 2 diabetes and wore a CGM for an average of 12 days. Scientists looked at glucose measurements over time and developed machine-learning models. The models determined if those values could indicate whether a person was healthy, prediabetic or diabetic.

Jeon noted that the 12-hour model showed similarly high accuracy compared to results from longer intervals. It correctly identified two-thirds of patients with prediabetes. The method also showed high accuracy in identifying healthy patients and those with type 2.

According to Klick, most research draws from 10 to 14 days of readings. It also often requires analysis from expert clinicians.

“An overwhelming majority of people with early-onset diabetes are not aware of their condition and don’t consult a physician until their ability to control their blood sugar levels is irreparably damaged,” said Michael Lieberman, managing director of R&D at Klick Applied Sciences. “Our research has tremendous potential to help move blood glucose digital biomarkers into a position where they can be an invaluable tool for physicians for preventing diabetes before it starts.”

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