#KlickHealth--A new research study in the IEEE Journal of Biomedical and Health Informatics reveals how scientists at Klick Labs created visual maps of sound frequencies (spectrograms) to identify com...

Klick Labs’ latest research drills deeper into vocal biomarker exploration without requiring participant age, BMI, or fixed voice sample
NEW YORK & TORONTO: #KlickHealth--A new research study in the IEEE Journal of Biomedical and Health Informatics reveals how scientists at Klick Labs created visual maps of sound frequencies (spectrograms) to identify complex, new patterns associated with Type 2 diabetes, using only smartphone recordings of randomly selected short English sentences and participant sex as study criteria.
The scientists analyzed 14,759 voice recordings from 461 adults, which were converted into a visual spectrogram before being analyzed by an Audio Spectrogram Transformer. The process allows the model to learn complex combinations of acoustic patterns directly from the recordings rather than relying primarily on predefined voice features selected in advance. Using voice only, the sex-stratified models achieved a balanced accuracy of 71.3 percent for women and 72.4 percent for men, with area under the receiver operating characteristic curve (AUC) values of 0.788 (78.8 percent) and 0.779 (77.9 percent), respectively.
“Now, we can literally see many more clues related to diabetes within vocal biomarkers,” said Yan Fossat, Senior Vice President, Digital Health R&D at Klick Applied Sciences and principal investigator of the study. “By capturing relationships across frequencies and time, we expanded our analysis beyond shimmer, jitter, and other predefined voice features and found complex combinations of acoustic patterns and features that weren’t on anyone’s radar. We are energized by the findings as we continue to explore associations between vocal biomarkers and Type 2 diabetes.”
Advancing the 2023 study
The research builds on Klick Labs’ 2023 Voice to Diabetes study in Mayo Clinic Proceedings: Digital Health, which analyzed fixed one-sentence smartphone voice recordings alongside participants’ age, body mass index (BMI), and other basic health information. The study identified 14 predefined acoustic features, including pitch, intensity, shimmer, and jitter, associated with Type 2 diabetes.
It should be noted that the new study reported balanced accuracy (which averages sensitivity and specificity, giving equal weight to identifying people with and without Type 2 diabetes), while the 2023 research reported conventional accuracy (the percentage of all predictions that are correct) of 89 percent for women and 86 percent for men, so the figures should not be compared directly. The difference in reported figures reflects the studies’ different inputs, methods, validation approaches, and datasets, not a claim that the new models are more accurate.
Over the last few years, Klick Labs has expanded its vocal biomarker research to areas, including hypertension and ovulation, while progressing its studies within diabetes exploring:
In June 2026, Klick Labs announced it has embarked on a series of studies with Mayo Clinic in Florida, exploring the use of novel vocal biomarkers in connection with a number of chronic diseases and serious health conditions.
About Klick Applied Sciences (including Klick Labs)
Klick Applied Sciences’ diverse team of data scientists, engineers, and biological scientists conducts scientific research and develops AI/ML and software solutions as part of the company’s work to support commercial efforts using its proven business, scientific, medical, and technological expertise. Klick Applied Sciences is part of the Klick Group of companies, which also includes Klick Health (including Oxford PharmaGenesis, Klick Katalyst, and btwelve), Klick Media Group, Klick Consulting, and Klick Ventures.
Fonte: Business Wire
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