A new study by Klick Labs reveals complex patterns in voice recordings that are associated with Type 2 diabetes. The research used 14,759 voice recordings from 461 adults, analyzing spectrograms and identifying acoustic patterns related to diabetes. The study achieved balanced accuracy of 71.3 percent for women and 72.4 percent for men.
Klick Labs is launching a series of clinical studies with Mayo Clinic exploring the use of novel vocal biomarkers in connection with Type 2 diabetes, hypertension, ovulation, and blood glucose. The research collaboration aims to leverage the voice's hidden properties to flag critical health issues and enhance patient care.
Researchers at Klick Labs developed an AI technique using vocal biomarkers to predict chronic high blood pressure with up to 84% accuracy. The study used machine learning to analyze hundreds of indiscernible vocal biomarkers, including pitch variability and speech energy distribution patterns.
A new study published in Scientific Reports confirms a linear relationship between blood glucose levels and voice fundamental frequency, suggesting potential for voice-based glucose monitoring. Researchers at Klick Labs used vocal biomarkers and AI to detect Type 2 diabetes with high accuracy.
Researchers at Klick Labs developed an algorithm to detect deepfakes with 80% accuracy by analyzing speech pause patterns, offering a solution to the growing problem of AI-generated content. The study's findings suggest that vocal biomarkers can distinguish between real and fake voices, providing a novel approach to flagging deepfakes.
Klick Applied Sciences unveils LOVENet, an AI framework that rapidly identifies new therapeutic indications for existing drugs. The algorithm integrates large language model and structured knowledge graph technology to offer a fresh perspective on new potential applications.
A new study by Klick Labs reveals that AI technology can screen for Type 2 diabetes using six to 10 seconds of a person's voice, with high accuracy rates. The research used acoustic features to analyze recordings from over 18,000 participants and identified significant vocal variations between individuals with and without the condition.
A new method of analysis flags impaired glucose homeostasis (IGH) in 20% of 'healthy' participants using continuous glucose monitors. This finding has significant implications for early detection and prevention of Type 2 diabetes, with the potential to impact millions worldwide.
Researchers at Klick Applied Sciences have created a machine learning model to predict diabetes onset in patients using just 12 hours of data from continuous glucose monitors. The study showed high accuracy in identifying prediabetes, healthy patients, and those with Type 2 diabetes, offering a potential tool for early disease prevention.