Scientists at Klick Applied Sciences have discovered a strategy to transform a continuous glucose monitor (CGM) into a strong diabetes screening and prevention tool using artificial intelligence.
In findings presented Friday on the NeurIPS conference in Recent Orleans, Klick scientists revealed how they used machine learning and just 12 hours of information from CGMs to find out whether a patient was prediabetic or diabetic.
We’ve demonstrated that 12 hours of monitoring could make a giant difference within the lives of individuals vulnerable to developing diabetes while there’s still time to course correct. We predict CGMs may very well be used to not only monitor diabetes–but to stop it altogether.”
Jouhyun Jeon, lead scientist of the study and principal investigator at Klick Applied Sciences
For the study, about 600 patients who identified as healthy, prediabetic, or living with Type 2 diabetes wore a CGM device for a mean of 12 days. The scientists checked out their glucose measurements over time and developed machine learning models to see if those values may very well be used to find out whether that person was healthy, prediabetic or diabetic.
Jeon said they found their 12-hour model showed similar high accuracy to results from the longer intervals, accurately identifying two-thirds of patients with prediabetes, while also showing high accuracy in identifying healthy patients and people with Type 2 diabetes. Jeon said the shorter timeframe is a giant step forward, adding most research draws from 10 to 14 days price of readings, and sometimes requires evaluation from expert clinicians.
In accordance with the CDC, prediabetes is a serious health condition where blood sugar levels are higher than normal, but not high enough yet to be diagnosed as Type 2 diabetes. Roughly 96 million American adults-;multiple in three-;have prediabetes. Of those with prediabetes, greater than 80 per cent do not know they’ve it.
“An amazing majority of individuals with early-onset diabetes will not be aware of their condition and do not seek the advice of a physician until their ability to manage their blood sugar levels is irreparably damaged,” said Michael Lieberman, managing director of research and development at Klick Applied Sciences. “Our research has tremendous potential to assist move blood glucose digital biomarkers right into a position where they might be a useful tool for physicians for stopping diabetes before it starts.”
These findings are the newest in Klick’s ongoing work within the diabetes space. Their “Homeostasis as a proportional–integral control system” study, based on mathematical modeling to find out a number of the underlying changes in how glucose is regulated that could cause diabetes, was published in Nature in 2020. Additionally they presented earlier findings on the 2018 International Joint Conference on Artificial Intelligence (IJCAI) in Stockholm, Sweden.