Determining whether a person is diabetic could be as easy as having them speak a few sentences into their smartphone, according to a groundbreaking study that combines voice technology with artificial intelligence in a major step forward in diabetes detection.
Scientists from Klick Labs in
the US used six to 10 seconds of people's voice, along with basic health data,
including age, sex, height, and weight, to create an AI model that can
distinguish whether that individual has Type 2 diabetes.
The model, detailed in the journal Mayo Clinic
Proceedings: Digital Health, has 89 per cent accuracy for women and 86 per cent
for men.
For the study, researchers
asked 267 people (diagnosed as either non- or Type 2 diabetic) to record a
phrase into their smartphone six times daily for two weeks. From more than
18,000 recordings, scientists analysed 14 acoustic features for differences
between non-diabetic and Type 2 diabetic individuals.
“Our research highlights significant vocal variations
between individuals with and without Type 2 diabetes and could transform how
the medical community screens for diabetes,” said Jaycee Kaufman, first author
of the paper and research scientist at Klick Labs.
“Current methods of detection
can require a lot of time, travel, and cost. Voice technology has the potential
to remove these barriers entirely.”
The team looked at a number of
vocal features, like changes in pitch and intensity that can't be perceived by
the human ear.
Using signalprocessing, scientists were able to detect
changes in the voice caused by Type 2 diabetes. Surprisingly, those vocal
changes manifested in different ways for males and females, Kaufman said.
Almost one in two, or 240
million adults living with diabetes worldwide are unaware they have the
condition and nearly 90 per cent of diabetic cases are Type 2 diabetes,
according to the International Diabetes Federation.
The most frequently used
diagnostic tests for prediabetes and Type 2 diabetes include the glycated
haemoglobin (A1C), along with the fasting blood glucose (FBG) test and the
OGTT.
Yan Fossat, vice president of Klick Labs and principal
investigator of this study, said the new non-intrusive and accessible approach
offers the potential to screen vast numbers of people and help identify the
large percentage of undiagnosed people with Type 2 diabetes.
“Our research underscores the
tremendous potential of voice technology in identifying Type 2 diabetes and
other health conditions,” Fossat said.
“Voice technology could
revolutionise healthcare practices as an accessible and affordable digital
screening tool.”
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