New Research Shows Artificial Intelligence (AI) May Replace Invasive Glucose Testing


Today, measuring blood glucose involves pricking a finger with a needle attached to a device. The blood sample is then analyzed by a continuous glucose monitor (CGM), which often needs to be calibrated at least twice a day. The process is a necessity but is difficult and uncomfortable.

The researchers at the University of Warwick are working on a new AI technology that can detect hypoglycemia using electrocardiogram (ECG) signals from the heart.

In their study, the scientists demonstrated that this new technology is accurate 82% of the time, a rate similar to that of current CGM systems. Senior study author Leandro Pecchia, Ph.D., an associate professor of biomedical engineering at the university said, “Our innovation consisted in using [AI] for automatic detecting [of] hypoglycemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping.”

Hypoglycemia affects the electrophysiology of the heart, and because it has slightly different effects on each individual’s heart, an AI system makes it possible to monitor glucose levels in a highly personalized way.

The team used AI to automatically detect nocturnal hypoglycemia from just a few heartbeat signals recorded by a wearable device. The study included healthy individuals, whom the scientists monitored for 24 hours a day for 14 consecutive days.

This study was unique because the scientists monitored the participants’ glucose levels individually, whereas previous trials had analyzed results from the participants as a group.

The researchers developed a way to visualize precisely which part of the ECG wave is associated with a hypoglycemic event. This could result in a real-time alarm system that alerts individuals if their blood sugar levels change dramatically. Having such an early warning could drastically shorten the amount of time that a person experiences hypoglycemia, which can be very dangerous, especially for people with diabetes.

The team’s new method is one example of precision medicine that could vastly improve the way that people manage diabetes. While there is still some way to go before this technology becomes available, the initial results are promising.

If successful, the technology tested in the present study could pave the way for many more uses of AI and electrophysiology of the heart. It could also possibly be used to manage a variety of disorders that result from changes in the blood, with highly personalized precision.


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