Vox
Reduce Heart Failure Hospitalizations by 50%
21-Day Advance Detection of Cardiac Decompensation
During cardiac decompensation, pulmonary edema and edema of the vocal folds can induce detectable alterations in a patient's voice. Noah Labs’ AI employs deep learning models, trained on proprietary datasets of audio recordings, to detect impending cardiac decompensation and allow clinicians to intervene early, ultimately avoiding hospitalization.
Listening to the heart
a sample of their voice via compatible devices
Longer, healthier lives despite heart failure
Early detection means timely intervention so that patients stay healthier and live longer.
Preventing heart failure deterioration reduces emergency visits and hospital stays.
Reducing avoidable hospital stays lowers costs for patients, providers, and payers.
Our journey towards clinical validation
VAMP-HF validates our voice-based algorithm's ability to distinguish between admission and discharge states in patients hospitalized with acute decompensated heart failure
This Noah Labs-led prospective trial aims to clinically validate our voice-based algorithm for MDR approval, in collaboration with leading university medical centers across Europe, supported by EU funding.
This study leverages 18 months of weekly voice recordings from chronic heart failure patients monitored at home after hospitalization to validate our algorithm’s performance for the early detection of heart failure decompensation.
VAMP-HF validates our voice-based algorithm's ability to distinguish between admission and discharge states in patients hospitalized with acute decompensated heart failure
Globally recognized pioneers in voice biomarker science
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Expert in signal processing & acoustics, advancing clinical voice research at MGH and Harvard.
Lecturer at King’s College London, specializing in AI-driven speech analysis for health and affective signal processing.
Postdoc at ETH Zurich researching mobile tech for chronic disease monitoring, with focus on AI and digital health tools.
Dr. Kamil Szyc is an assistant professor at the Wroclaw University of Science and Technology focused on computational signal processing and machine learning technologies.
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