Researchers at Klick Labs have developed a machine learning model that can detect Type 2 diabetes from a 6 to 10 second voice recording with up to 89% accuracy for women and 86% accuracy for men. The method analyzes acoustic features in the voice and can potentially transform diabetes screening. The researchers believe this technology could be expanded to diagnose other health conditions as well. However, the study has a relatively small sample size and there is limited information about the severity of diabetes in the diabetic group. Voice analysis combined with BMI yielded the best results.
Researchers diagnose diabetes in seconds using voice recordings
Researchers at healthcare research company Klick Labs have developed a machine learning model that can determine the presence of Type 2 diabetes from a brief voice recording of just 6 to 10 seconds. The model shows a maximum test accuracy rate of 89% for women and 86% for men when combined with other metrics like body mass index (BMI).
The study involved the analysis of 18,000 recordings, aiming to identify unique acoustic features that distinguish diabetic from non-diabetic individuals, detecting subtle differences in pitch and intensity imperceptible to the human ear.
The researchers believe that this voice technology could revolutionize healthcare practices as an accessible and affordable digital screening tool. It has the potential to remove barriers such as time, travel, and cost associated with current methods of detection.
The method requires a short smartphone-recorded audio clip, making it convenient and noninvasive. By analyzing the voice, the technology can identify the vocal variations that indicate the presence of Type 2 diabetes.
Not only can this technology be used for diabetes detection, but the researchers also believe that it can be extended to diagnose other health conditions. This highlights the growing role of AI in healthcare.
While the study’s sample size is relatively small and there are some limitations in terms of advanced diabetes information, the results show promise, particularly for individuals with advanced or unmanaged forms of the disease.
Key Findings:
– Voice analysis accurately predicts the presence of Type 2 diabetes.
– Maximum test accuracies reached 89% for women and 86% for men when combined with BMI data.
– The method is convenient, accessible, and noninvasive, using short smartphone-recorded audio clips.
– Voice technology has the potential to revolutionize healthcare practices and remove barriers associated with current detection methods.
– The researchers believe this technology can be extended to diagnose other health conditions.
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