” Our research highlights substantial vocal variations between individuals with and without Type 2 diabetes and might transform how the medical community screens for diabetes,” said Jaycee Kaufman, first author of the paper and research study researcher at Klick Labs. A brand-new medical research study by Klick Labs discovered that AI and 10 seconds of voice could alter the way individuals screen for diabetes, providing much better gain access to and lower costs than current screening techniques. The findings, released in Mayo Clinic Proceedings: Digital Health, reported 89 percent accuracy for women and 86 percent for males in anticipating Type 2 diabetes from acoustic voice functions. Using signal processing, researchers were able to find modifications in the voice triggered by Type 2 diabetes.
A new research study reveals that examining a couple of seconds of an individuals voice using AI can determine if they have Type 2 diabetes with up to 89% accuracy. This non-intrusive technique has the prospective to transform diabetes screening by removing current detection barriers like time, cost, and travel.
Researchers at Klick Labs recognize voice innovation as a prospective development in finding Type 2 diabetes.
Determining if somebody has diabetes might quickly be as simple as them uttering a few expressions into their mobile phone, suggests a pioneering study from Klick Labs. This research combines voice recognition technology and synthetic intelligence, marking a significant improvement in the field of diabetes recognition.
The brand-new study, released in Mayo Clinic Proceedings: Digital Health, describes how scientists utilized six to 10 seconds of peoples voice, in addition to standard health data, including age, sex, weight, and height, to develop an AI model that can differentiate whether that individual has Type 2 diabetes. The design has 89 percent accuracy for women and 86 percent for men.
For the study, Klick Labs scientists asked 267 individuals (identified as either non- or Type 2 diabetic) to tape-record an expression into their smart device six times daily for two weeks. From more than 18,000 recordings, scientists analyzed 14 acoustic features for distinctions between non-diabetic and Type 2 diabetic individuals.
” Our research highlights substantial vocal variations between individuals with and without Type 2 diabetes and might transform how the medical community screens for diabetes,” said Jaycee Kaufman, very first author of the paper and research researcher at Klick Labs. “Current methods of detection can require a great deal of time, travel, and expense. Voice technology has the potential to eliminate these barriers totally.”
A brand-new scientific study by Klick Labs discovered that AI and 10 seconds of voice could alter the way individuals screen for diabetes, offering better gain access to and lower costs than current screening approaches. The findings, published in Mayo Clinic Proceedings: Digital Health, reported 89 percent accuracy for females and 86 percent for males in anticipating Type 2 diabetes from acoustic voice functions. Credit: Klick Labs
The group at Klick Labs looked at a variety of vocal features, like changes in pitch and intensity that cant be viewed by the human ear. Utilizing signal processing, scientists had the ability to spot changes in the voice triggered by Type 2 diabetes. Surprisingly, those vocal changes manifested in various methods for women and males, Kaufman stated.
A Potential New Screening Tool for Undiagnosed Diabetes
Almost one in 2, or 240 million grownups dealing with diabetes around the world are unaware they have the condition and nearly 90 percent of diabetic cases are Type 2 diabetes, according to the International Diabetes Federation. The most frequently utilized diagnostic tests for prediabetes and Type 2 diabetes include the glycated hemoglobin (A1C), in addition to the fasting blood sugar (FBG) test and the OGTT– all of which include a journey to a doctor for clients.
Yan Fossat, vice president of Klick Labs and principal investigator of this research study, stated Klicks accessible and non-intrusive approach offers the prospective to screen huge varieties of people and help identify the big percentage of undiagnosed people with Type 2 diabetes.
” Our research underscores the tremendous capacity of voice innovation in recognizing Type 2 diabetes and other health conditions,” Fossat said. “Voice technology might revolutionize healthcare practices as a economical and available digital screening tool.”
Fossat said next steps will be to reproduce the research study and broaden their research utilizing voice as a diagnostic in other locations such as prediabetes, femaless health, and hypertension.
Reference: “Acoustic Analysis and Prediction of Type 2 Diabetes Mellitus Using Smartphone-Recorded Voice Segments” by Jaycee M. Kaufman, Anirudh Thommandram and Yan Fossat, 17 October 2023, Mayo Clinic Proceedings: Digital Health.DOI: 10.1016/ j.mcpdig.2023.08.005.
