Using daily voice recordings to spot pulmonary artery pressure changes in people with heart failure
Official title: Voice Analysis Using Artificial Intelligence to Detect Changes in Pulmonary Arterial Pressure in Patients With Heart Failure and an Implanted Pressure Sensor
Run by Noah Labs · NCT07443670 · Data updated 2026-11-12
We will test whether daily voice recordings from people with heart failure and implanted pulmonary artery pressure sensors can help an AI detect rises in lung blood pressure.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 60 (estimated) |
| Sponsor | Noah Labs Industry-sponsored |
| Locations | 3 sites (San Francisco, California and 2 other locations) |
| Status | Recruiting |
| Started | December 12, 2024 |
| Main results expected | May 2026 |
| Study ends | September 2026 |
| Registry record updated | March 2, 2026 |
| Trial ID | NCT07443670 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults with heart failure who already have an implanted PA pressure sensor, can read aloud in English or German, and can use a smartphone or tablet to record daily voice samples are ideal candidates.
✕Probably not a fit
People without implanted PA sensors, those with pathological voice changes, COPD on home oxygen, dialysis-dependent kidney failure, pregnancy, or inability to reliably use a smartphone are unlikely to benefit from this approach.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This is a prospective, multi-center observational study in which participants with implanted pulmonary artery (PA) pressure sensors provide daily voice recordings via a mobile app for 12 weeks while continuing standard PA pressure monitoring and heart failure care. Voice samples include sustained vowel sounds and a standardized reading passage and are collected on participants' smartphones or tablets. PA pressure readings obtained as part of routine care are paired with the voice data and analyzed retrospectively using classical machine learning and deep learning methods. The primary endpoint is the sensitivity and specificity of the AI-based voice analysis in detecting PA pressure changes at prespecified thresholds, and no clinical decisions are made based on voice results during the study.
Why it matters
Potential benefit: If successful, this approach could provide a low-cost, non-invasive way to detect pulmonary congestion earlier and reduce reliance on invasive sensors.
How similar studies have gone: Early research has suggested voice features can reflect respiratory and fluid status, but applying AI to detect PA pressure changes in patients with implanted sensors is a novel and not yet validated strategy.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Age 18 years or older * Successful implantation of a PA pressure sensor and monitored by a participating study center * Willingness to record a short predefined text daily for 3 months using a smartphone or tablet * Ability to comfortably read aloud the study passage in English or German * Written informed consent obtained Exclusion Criteria: * Pregnant, breastfeeding, or unwilling to practice birth control during participation * Condition that in the opinion of the investigator would compromise patient safety or data quality * Pathological voice changes due to surgery or injury * Planned invasive cardiac procedures during the study period * COPD requiring home oxygen therapy * Chronic kidney disease requiring dialysis * Cognitive dysfunction limiting ability to perform daily voice recording * Inability to read English or German * Physical inability to use the recording device
Where it's running
-
University of California, San Francisco (UCSF)
San Francisco, California, United States Recruiting -
BG Klinikum Unfallkrankenhaus Berlin, Dept. of Cardiology
Berlin, State of Berlin, Germany Completed -
University Hospital Frankfurt, Dept. of Cardiology and Angiology
Frankfurt, Germany Completed
Study contacts
- Study coordinator: Leonhard Riehle, MD
- Email: leonhard.riehle@noah-labs.com
- Phone: +491715547970
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
-
Using Apple Watch data to predict heart failure outcomes
This study is testing if data from Apple Watches can help predict heart failure problems and the chances of needing to go back to the hospital for patients with heart failure.
-
Comparing Aortix Device to Diuretics for Acute Heart Failure
This study is testing whether the Aortix device can help people with acute heart failure who aren't responding to diuretics feel better compared to standard treatment.
-
Myocardial Energetics Across Cardiovascular Disease Spectrum Using 31P-MRS
Researchers are testing whether phosphorus-31 magnetic resonance spectroscopy (31P-MRS) can map heart energy metabolism across healthy people and patients with various heart conditions.
-
Tailored digital support to cut sedentary time in long-term cardiac rehab
In adults in a long-term cardiac rehabilitation program, this study tests whether a personalized behavioural program with digital tools can reduce daily sitting time.
-
NT-proBNP–guided heart failure screening for people 65+ in rural China
This study tests whether using NT-proBNP blood tests to guide heart failure screening in adults 65 and older in rural China can find unrecognized heart problems more effectively than symptom-based screening.
-
Voice-based detection of heart failure deterioration using Noah Labs Vox
This study tests whether a voice-analysis app can flag worsening heart failure in adults by comparing its alerts to standard weight-based monitoring.
How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.