Research team developed a machine learning algorithm to predict whether people had T2D, prediabetes, or no diabetes. MedicalAI
the researchers describe how their algorithm sorted people into these three categories with 97% accuracy on the basis of measurements of the heart's electrical activity, determined from anTo develop and train their machine learning model — a type of artificial intelligence that keeps getting smarter over time — researchers used ECG measurements from 1262 people in Central India.
"ECG has the power to detect these fluctuations, at least in theory, but those fluctuations are so tiny that many times we as humans looking at that might miss it," says study author Hemant Kulkarni, MD, chief executive officer of M&H Research LLC and president of the Lata Medical Research Foundation."But the AI, which is powered to detect such specific fluctuations or subtle features, we hypothesized for the study that the AI algorithm might be able to pick those things up.
The team hopes to test and validate the algorithm in a variety of populations so that it can eventually be developed into an accessible, user-friendly technology. They envision that someday their algorithm could be used in smartwatches or other smart devices and could be integrated into telehealth so that people could be screened for diabetes even if they weren't able to travel to a healthcare facility for blood testing.
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