Artificial intelligence was used to develop new technology capable of detecting low glucose levels via ECG using a non-invasive sensor. It is able to detect hypoglycaemic events from raw ECG signals. The technology was developed by researchers from the University of Warwick, including Dr. Leandro Pecchia.
Continuous Glucose Monitors (CGM) are currently used, and they are available for hypoglycaemic detection. They are able to measure glucose in interstitial fluid through the use of an invasive senor with a small needle. This then sends alarms and data to a display device. Often times, they need to be calibrated twice a day with invasive finger-prick blood glucose level tests.
Dr. Leandro Pecchia’s team at the University of Warwick published their results on January 13th in a paper titled “Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG.” It was published in the Nature Springer journal Scientific Reports.
The paper proved that the latest developments in artificial intelligence (deep learning) can be used to detect hypoglycaemic events from raw ECG signals that are acquired through non-invasive wearable sensors.
There were two pilot studies conducted with healthy volunteers, and they found that the average sensitivity and specificity hypoglycaemic detection is comparable with current CGM performance, but it is non-invasive.
Dr. Leandro Pecchia is from the School of Engineering at the University of Warwick.
“Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpick during the night certainly is unpleasant, especially for patients in paediatric age.
“Our innovation consisted in using artificial intelligence for automatic detecting hypoglycaemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping.”
The model used by the researchers is called the Warwick model, and it highlights how the ECG changes in each subject during a hypoglycemic event. The AI model was trained by the researchers with each subject’s own data. Since there are so many intersubjective differences, using cohort data to train the system would not give the same results. A more effective approach would be personalized therapy based on the new system.
It is likely that the Warwick scientists’ method was so effective because the AI algorithms are trained with the subject’s own data.
“The performance of AI algorithms trained over cohort ECG-data would be hindered by these inter-subject differences,” says Pecchia.
“Our approach enables personalized tuning of detection algorithms and emphasize how hypoglycemic events affect ECG in individuals. Basing on this information, clinicians can adapt the therapy to each individual. Clearly more clinical research is required to confirm these results in wider populations. This is why we are looking for partners.”
Right Around the Corner
Artificial intelligence within the medical field is one of the major potential uses for this technology. The current applications are already extremely impressive, and they will continue to advance. This new technology can solve one of the most uncomfortable daily aspects of diabetics, and it very well can bring an end to the finger-prick tests required.
Often times, the focus is on major medical advancements that can take place because of artificial intelligence, such as curing diseases and performing extremely precise surgical operations. This is all true, and it will undoubtedly bring major advancements to the medical field, which will then do the same to society. There will come a time when robots are performing most surgical procedures, developing pharmaceuticals and cures, and almost everything else imaginable. While this is not far away, nobody knows the exact time it will take to reach that point. However, with the type of technology developed by the researchers at the University of Warwick, or other advancements in robotics such as prosthetics and artificial skin, artificial intelligence will soon change the daily lives of people living with these medical conditions. We don’t have to wait for the future to see the major medical advancements, technology that will drastically change hundreds of millions of lives is right around the corner.