Ayush Darpan ISSNN0.0976-3368

Health awareness across the globe….

How AI can detect diabetes with a 10-second voice sample

8 min read

Artificial intelligence can analyse speech patterns to detect type 2 diabetes with astonishing accuracy. The method could prove to be a useful diagnostic tool. But it comes with a warning label. Medical diagnostic tools using advanced voice analysis are becoming increasingly precise. Analysing speech patterns can provide valuable insights, particularly for diseases such as Parkinson’s or Alzheimer’s.Mental illness, depression, post-traumatic stress disorder and heart disease can also be detected using voice analysis. Artificial intelligence (AI) can even detect signs of constricted blood vessels or exhaustion. This allows medical professionals to treat patients sooner and reduce any possible risks. According to a study published in the Mayo Clinic Proceedings: Digital Health medical journal, a short voice recording is all it takes to determine with surprising accuracy whether an individual has type 2 diabetes.

This technology is intended to help identify people living with undiagnosed diabetes. Worldwide, some 240 million adults have diabetes and don’t know it. Nearly 90 per cent of cases are type 2 diabetes, according to the International Diabetes Federation. People with type 2 diabetes have an elevated risk of cardiovascular diseases, such as heart attack, stroke and poor circulation in the legs and feet.Diabetes screening tests using voice analysis would significantly improve detection. Most other tests require a trip to a health care provider. These include the fasting blood glucose test (FBG), the oral glucose tolerance test (OGTT), or the glycated haemoglobin test (A1C). The latter is performed to measure average blood sugar levels over the course of two to three months.How does voice analysis work?

With voice frequency analysis, changes in the voice that are inaudible to the human ear are analysed by AI. Oftentimes, recordings of a phone conversation are all the software needs for the analysis.It examines factors such as speech melody, cadence, pauses and pitch. Certain symptoms have characteristic phonetic traits, such as how the vowel A is pronounced over a period of five seconds. The human voice can display up to 200,000 distinct characteristics. AI algorithms can filter through all of these to identify particular vocal patterns that match certain symptoms.

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