Type 2 diabetes (T2D) affects millions worldwide and remains undiagnosed in many individuals. Traditional screening methods are not overly complex, but they involve invasive blood tests which can be a deterrent to some people. These tests are also difficult to scale in some parts of the world. Meanwhile, the prevalence of diabetes continues to soar, with estimates predicting 783 million cases globally by 2045 — or 1 in 7 adults. To avoid a massive global crisis, we need new, easier way to diagnose diabetes. And that’s exactly what researchers from Luxembourg have developed.
They found a new way to diagnose diabetes using voice samples of all things. The Colive Voice study, conducted by an international team of researchers, explores how subtle differences in vocal characteristics can identify T2D with remarkable accuracy. If this can be scaled, it could be a potential game-changer in medical diagnostics.
Hearing diabetes
You may wonder how diabetes, a metabolic disorder, can influence your voice. To the human ear, any difference will likely be too small to pick up, but there are some subtle differences that algorithms can notice.
The connection lies in the physiological effects of diabetes on your respiratory, neuromuscular, and overall health. Conditions like poor glycemic control and peripheral neuropathy can impair the small muscles and nerve networks involved in voice production. As a result, people with diabetes often exhibit subtle vocal changes such as hoarseness, reduced pitch stability, or altered breathing patterns during speech.
These changes show up in features like jitter, shimmer, and harmonic ratios. By comparing these vocal parameters with data from healthy individuals, algorithms can detect patterns indicative of diabetes.
In the new study, researchers analyzed voice recordings from 607 U.S. adults using a combination of traditional voice features and cutting-edge machine learning models. Participants read a standardized text, enabling the algorithm to identify subtle vocal differences.
How good is it?
The algorithm predicted diabetes for 71% of male and 66% of female participants. It performed even better for people over 60 years old and suffering from hypertension.
“This research represents a major step in diabetes care. By combining AI with digital phenotyping, we are ushering in a more inclusive and cost-effective approach to early diagnosis and prevention. The ability to screen for diabetes using a simple voice recording could dramatically improve healthcare accessibility for millions of people around the world,” said Dr. Guy Fagherazzi
While the results are promising, there are hurdles to overcome before widespread adoption. The study’s reliance on English-speaking participants limits its generalizability to diverse populations. Expanding the dataset to include other languages and cultural contexts is crucial.
As diabetes continues to challenge public health systems worldwide, embracing such advancements could redefine the landscape of preventive care, ensuring timely treatment and better outcomes for millions.
The study “A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study” has been published in the journal PLoS Digital Health.
