Researchers Develop AI Model To Predict Type 2 Diabetes Onset Within 12 Hours

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Researchers Develop AI Model To Predict Type 2 Diabetes Onset Within 12 Hours

Type 2 diabetes is a common health condition which causes the level of sugar or glucose levels in the blood to become too high. It leads to symptoms such as excessive thirst, needing to pee a lot and extreme tiredness and fatigue. Diabetes also increases a person’s risk of getting serious and chronic health problems concerning the eyes, heart and nerves.

According to a recent study, researchers developed an artificial intelligence (AI) model which helps in predicting the onset of type-2 diabetes within 12 hours time. Researchers investigated if or not they could use readings from continuous glucose monitors (CGM) to accurately diagnose prediabetes and diabetes in people. The researchers could classify type 2 diabetes, prediabetes, and people without impaired glucose tolerance in just 12 hours of glucose profile data.

“I believe our method of diagnosis helps in offering a lot of potential to be used as a novel tool to aid healthcare providers in their own decision-making. Especially for remote or virtual care of diabetes patients. For the general public, our method could not only be used for accurately monitoring and early screening but alerting a patient of their risk of developing diabetes,” said Jouhyun Clare Jeon, Ph.D., principal investigator at Klick Applied Sciences and lead author of the study told a leading medical website.

Researchers Develop AI Model To Predict Type 2 Diabetes Onset Within Twelve Hours

Also read: Expert Explains Ways To Keep Heart Healthy

Upon analysis the researchers found, participants’ A1C levels of 6.5% and higher as type 2 diabetes, 5.5% to 6.5% as prediabetic, and under 5.5% as healthy individuals.

“The findings of our research are developed based on about the CGM signals of 400 patients. There is further evaluation required using an independent larger cohort and bigger population data in order to generalize our way and method. However, we are encouraged by the results and look forward to our continued work in the same area,” concluded Dr. Jeon.

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