Researchers from the Institute of Science Tokyo and other institutions have developed an AI model that detects high diabetes risk using only electrocardiogram data. A team of researchers from organizations such as the Institute of Science Tokyo said Tuesday it has developed an artificial intelligence model that can detect a high risk of diabetes using only electrocardiogram (EKG) data. This method, which does not require blood tests, can lead to possible early detection of the disease and help those at high risk review their lifestyles, according to the team. Together with other team members, Tetsuya Yamada, a professor at the university, divided about 16,000 people who underwent medical checkups in Tokyo in 2022 into a group of diabetics and prediabetics, with higher-than-standard blood sugar levels, and a group of subjects with normal readings. The team put its EKG data into an AI model to analyze minuscule changes in cardiac muscle movement that appear in the prediabetic stage.

In the near future, doctors may be able to apply artificial intelligence to electrocardiogram data in order to measure overall health status, according to new research published in Circulation: Arrhythmia and Electrophysiology, a journal of the American Heart Association. An electrocardiogram, also known as an EKG or ECG, is a test used to measure the electrical activity of the heart. While it's known that a patient's sex and age could affect an EKG, researchers hypothesized that artificial intelligence could determine a patient's gender and estimate their'physiologic age' -- a measure of overall body function and health status distinct from chronological age. Using EKG data of almost 500,000 patients, a type of artificial intelligence known as a convolutional neural network was trained to find similarities among the input and output data. Once trained, the neural network was tested for accuracy on the data of an additional 275,000 patients by predicting the output when only given input data.