Scientists have Artificial intelligence has discovered a way to check whether a person has just spoken a few sentences into a smartphone with the help of Diabetes Is it or not?
A team from the US organization ‘Click Labs’ has created a model using artificial intelligence that can distinguish whether a person has type 2 diabetes or not after listening to an audio for six to 10 seconds. The phonetic test showed an accuracy rate of 89 percent for women and 86 percent for men.
Click Labs Scientist Jesse Kaufman said: ‘Our research highlights important vocal differences between people with type 2 diabetes and healthy individuals and could change the way diabetes screening is done for the medical community. is.’
According to him: ‘Current methods of detecting this disease require a lot of time, travel and cost. Sound technology has the potential to completely remove these barriers.’
The study analyzed 18,000 recordings to identify acoustic features that differed between diabetics and healthy individuals. Using signal processing they were able to detect subtle changes in pitch and intensity. The human ear cannot perceive this difference.
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According to data from the International Diabetes Federation, the app can be useful for an estimated 240 million adults worldwide who are currently living with the disease.
The latest research shows the growing role of artificial intelligence in healthcare, with machine learning models, data science improving patient care and aiding in clinical discoveries.
The researchers claim that this artificial intelligence model, which uses basic health data to determine whether a person is diabetic or not, can be extended to diagnose other diseases.
Ian Foust, vice president and research leader at ClickLabs, said: ‘Our research highlights the tremendous potential of voice technology in identifying type 2 diabetes and other diseases.’
According to him: ‘Voice technology can revolutionize healthcare practices as an accessible and affordable digital screening tool.’
The technology is detailed in a study titled ‘Acoustic analysis and prediction of Type 2 Diabetes Mellitus using smartphone-recorded voice segments’, published in the journal ‘Mayo Clinic Proceedings: Digital Health’.
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