(Web Desk) - Canadian medical researchers have trained a machine-learning AI to accurately predict Type 2 diabetes from just six to 10 seconds of the patient's spoken voice.
This was achieved after the model identified 14 acoustic features for differences between non-diabetic and Type 2 diabetic individuals.
The AI focused on a set of vocal features, including slight changes in pitch and vocal intensity that the human ears can't hear of doctors, and paired that data with basic health information, including the patient's age, sex, height, and weight.
Sex proved to be decisive, the researchers found: The AI can diagnose the disease with 89 percent for women, but slightly less accurately, 86 percent for men.
The AI model promises to dramatically reduce the cost for ordinary people suffering from the chronic health condition, which traditionally must be tested in person.
'Our research highlights significant vocal variations between individuals with and without Type 2 diabetes,' said Jaycee Kaufman, first author of the paper and a research scientist at Klick Labs, which plans to market the software.
Kaufman hopes the company's AI could 'transform how the medical community screens for diabetes.'
In the past, costly in-person diagnostic tests, including blood work, have been required to screen for prediabetes and Type 2 diabetes.
Among the most common tests employed are the glycated hemoglobin (A1C) test, the fasting blood glucose (FBG) test and the oral glucose tolerance tests (OGTT), which all require patients to make a physical trip to their healthcare provider.