New research to be presented at this year's Annual Meeting of The European Association for the Study of Diabetes (EASD), Madrid highlights the potential of using voice analysis to detect undiagnosed type 2 diabetes (T2D) cases.
From a total of 607 recordings, the AI algorithm analysed various vocal features, such as changes in pitches, intensity, and tone, to identify differences between individuals with and without diabetes.
This was done using two advanced techniques: one that captured up to 6,000 detailed vocal characteristics, and a more sophisticated deep-learning approach that focused on a refined set of 1,024 key features.
The study used on average 25 seconds of people's voices along with basic health data including age, sex, body mass index (BMI), and hypertension status, to develop an AI model that can distinguish whether an individual has T2D or not, with 66% accuracy in women and 71% accuracy in men.
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