AI replaces prick test to detect blood sugar levels

AI replaces prick test to detect blood sugar levels

Tracking fluctuations in blood glucose levels is crucial for monitoring diabetic patients. Strict glucose monitoring reduces the risk of hypoglycemia, which can be potentially fatal if it’s not treated promptly. Previously, glucose monitoring is performed through a needle prick test or capillary blood glucose test. Now, thanks to artificial intelligence (AI), doctors can monitor blood sugar levels using a few heartbeats of raw ECG signals recorded through wearable sensors. A team of researchers at the University of Warwick developed a new technology to detect low glucose levels through an electrocardiogram (ECG) using a noninvasive wearable device. The device is tied with the latest artificial intelligence that can detect hypoglycemic evens from raw ECG signals. At present, patients who are required to undergo continuous tracking of blood sugar levels are given continuous glucose monitors (CGM) for hypoglycemia detection. However, CGM uses an invasive needle that sends alarms when the blood sugar levels become low. The device needs calibration two times a day with invasive finger-prick blood tests. Credit: University of Warwick AI works just as well The novel artificial intelligence system works as well as CGMs, providing data on glucose levels without invasive needles. The researchers tested the device in two pilot studies with healthy participants and found that the average sensitivity and specificity was 82 percent in detecting hypoglycemia, which works just as well as the current CGM method. “Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpick during the night certainly is unpleasant, especially for patients in pediatric age. Our innovation consisted in using artificial intelligence for automatic detecting hypoglycemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping,” Dr. Leandro Pecchia from the School of Engineering at the University of Warwick, said. Trained with the participant’s own data Related Stories



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