A team of Indian scientists have developed an artificial intelligence (AI) algorithm that is derived from the features of individual heartbeats recorded on an ECG (electrocardiogram), and can accurately predict diabetes and pre-diabetes. The team from Lata Medical Research Foundation in Nagpur included clinical data from 1,262 individuals. A standard 12-lead ECG heart trace lasting 10 seconds was done for each of the participants. And 100 unique structural and functional features for each lead were combined for each of the 10,461 single heartbeats recorded to generate a predictive algorithm named DiaBeats. Based on the shape and size of individual heartbeats, the DiaBeats algorithm quickly detected diabetes and prediabetes with an overall accuracy of 97 per cent and a precision of 97 per cent, irrespective of influential factors, such as age, gender, and co-existing metabolic disorders.

An artificial intelligence (AI) algorithm, derived from the features of individual heartbeats recorded on an ECG (electrocardiogram), can accurately predict diabetes and pre-diabetes, suggests preliminary research published in the online journal BMJ Innovations. If validated in larger studies, the approach could be used to screen for the disease in low resource settings, say the researchers. An estimated 463 million adults around the world had diabetes in 2019, and picking up the disease in its early stages is key to preventing subsequent serious health problems. But diagnosis relies heavily on the measurement of blood glucose. This is not only invasive but also challenging to roll out as a mass screening test in low resource settings, point out the researchers.