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New AI technology uses voice analysis to detect undiagnosed type 2 diabetes
DiabetologiaSep 9 2024 New research to be presented at this year's Annual Meeting of The European Association for the Study of Diabetes (EASD), Madrid (9-13 Sept), highlights the potential of using voice analysis to detect undiagnosed type 2 diabetes (T2D) cases. 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. Most current methods of screening for type 2 diabetes require a lot of time and are invasive, lab-based, and costly. Combining AI with voice technology has the potential to make testing more accessible by removing these obstacles. This study is the first step towards using voice analysis as a first-line, highly scalable type 2 diabetes screening strategy." Abir Elbeji, lead author from the Luxembourg Institute of Health, Luxembourg Around half of adults with diabetes (around 240 million worldwide) are unaware that they have the condition because the symptoms can be general or non-existent-;around 90% of these have T2D. But early detection and treatment can help prevent serious complications. Reducing undiagnosed T2D cases worldwide is an urgent public health challenge. The study set out to develop and assess the performance of a voice-based AI algorithm to detect whether adults have T2D. Researchers asked 607 adults from the Colive Voice study (diagnosed with and without T2D) to provide a voice recording of themselves reading a few sentences of a provided, directly from their smartphone or laptop. Both females and males with T2D were older (average age females 49.5 vs 40.0 years and males 47.6 vs 41.6 years) and were more likely to be living with obesity (average BMI females 35.8 vs 28.0 kg/m² and males 32.8 vs 26.6 kg/m²) than those without T2D. 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 performance of the best models was grouped by several diabetes risk factors including age, BMI, and hypertension, and compared to the reliable American Diabetes Association (ADA) tool for T2D risk assessment. The voice-based algorithms showed good overall predictive capacity, correctly identifying 71% of male and 66% of female T2D cases. The model performed even better in females aged 60 years or older and in individuals with hypertension. Additionally, there was 93% agreement with the questionnaire-based ADA risk score, demonstrating equivalent performances between voice analysis and a widely accepted screening tool. "While our findings are promising, further research and validation are necessary before the approach has the potential to become a first-line diabetes screening strategy and help reduce the number of people with undiagnosed type 2 diabetes. Our next steps are to specifically target early-stage type 2 diabetes cases and pre-diabetes," said co-author Dr Guy Fagherazzi from the Luxembourg Institute of Health, Luxembourg. Diabetologia
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Voice analysis can screen people for type 2 diabetes with high accuracy, study finds
New research presented at the Annual Meeting of The European Association for the Study of Diabetes (EASD), Madrid (9-13 Sept), highlights the potential of using voice analysis to detect undiagnosed type 2 diabetes (T2D) cases. 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. "Most current methods of screening for type 2 diabetes require a lot of time and are invasive, lab-based, and costly," explained lead author Abir Elbeji from the Luxembourg Institute of Health, Luxembourg. "Combining AI with voice technology has the potential to make testing more accessible by removing these obstacles. This study is the first step towards using voice analysis as a first-line, highly scalable type 2 diabetes screening strategy." Around half of adults with diabetes (around 240 million worldwide) are unaware that they have the condition because the symptoms can be general or non-existent -- around 90% of these have T2D. But early detection and treatment can help prevent serious complications. Reducing undiagnosed T2D cases worldwide is an urgent public health challenge. The study set out to develop and assess the performance of a voice-based AI algorithm to detect whether adults have T2D. Researchers asked 607 adults from the Colive Voice study (diagnosed with and without T2D) to provide a voice recording of themselves reading a few sentences of a provided, directly from their smartphone or laptop. Both females and males with T2D were older (average age females 49.5 vs. 40.0 years and males 47.6 vs. 41.6 years) and were more likely to be living with obesity (average BMI females 35.8 vs. 28.0 kg/m and males 32.8 vs. 26.6 kg/m) than those without T2D. From a total of 607 recordings, the AI algorithm analyzed 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 performance of the best models was grouped by several diabetes risk factors including age, BMI, and hypertension, and compared to the reliable American Diabetes Association (ADA) tool for T2D risk assessment. The voice-based algorithms showed good overall predictive capacity, correctly identifying 71% of male and 66% of female T2D cases. The model performed even better in females aged 60 years or older and in individuals with hypertension. Additionally, there was 93% agreement with the questionnaire-based ADA risk score, demonstrating equivalent performances between voice analysis and a widely accepted screening tool. "While our findings are promising, further research and validation are necessary before the approach has the potential to become a first-line diabetes screening strategy and help reduce the number of people with undiagnosed type 2 diabetes. Our next steps are to specifically target early-stage type 2 diabetes cases and pre-diabetes," said co-author Dr. Guy Fagherazzi from the Luxembourg Institute of Health, Luxembourg.
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Researchers have developed an AI-based voice analysis technology that can detect undiagnosed type 2 diabetes. This innovative approach could revolutionize early screening and diagnosis of the disease.
Researchers have made a groundbreaking advancement in the field of medical diagnostics, developing an artificial intelligence (AI) system capable of detecting undiagnosed type 2 diabetes through voice analysis. This novel approach could potentially revolutionize the way we screen for and diagnose this prevalent metabolic disorder 1.
The AI technology focuses on analyzing specific voice features that may be indicative of type 2 diabetes. These features include changes in the voice caused by the disease's impact on the speaker's vocal folds and larynx muscles. The system examines various aspects such as frequency, amplitude, and mel-frequency cepstral coefficients (MFCCs) to identify potential markers of diabetes 2.
A team of researchers conducted a study involving 267 participants who were asked to record voice samples. The AI system analyzed these samples and successfully identified individuals with type 2 diabetes with an impressive accuracy rate of 89% 1. This high level of accuracy demonstrates the potential of this technology as a reliable screening tool for diabetes.
The development of this AI-powered voice analysis tool could have far-reaching implications for public health. Type 2 diabetes often goes undiagnosed in its early stages, leading to complications and increased healthcare costs. This non-invasive screening method could enable earlier detection and intervention, potentially improving patient outcomes and reducing the burden on healthcare systems 2.
Traditional diabetes screening methods typically involve blood tests, which can be invasive, time-consuming, and costly. The voice analysis approach offers several advantages, including:
These benefits could make diabetes screening more accessible, especially in underserved or remote areas 1.
While the results are promising, researchers emphasize the need for further studies to validate the technology's effectiveness across diverse populations. Additionally, work is ongoing to refine the AI algorithms and expand the database of voice samples to improve accuracy and reliability 2.
Experts envision integrating this voice analysis technology with existing digital health platforms and smartphone apps. This integration could allow for widespread, easily accessible diabetes screening, potentially reaching millions of people who might otherwise remain undiagnosed 1.
As with any AI-based health technology, there are important ethical considerations and privacy concerns to address. Ensuring the protection of personal health information and preventing misuse of the technology will be crucial as it moves towards potential implementation in clinical settings 2.
Reference
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Medical Xpress - Medical and Health News
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