AI Stethoscope Tested for Detecting Pulmonary Hypertension and Heart Disease
Tests an AI stethoscope to detect pulmonary hypertension and heart disease in adults.
Plain-language title generated from the registry record
Registry title: Pulmonary Arterial Hypertension and Associated Cardiovascular Disease Detection Using Artificial Intelligence
Key facts
- Where
- London, United KingdomSee the site
Where in United Kingdom
- Imperial College Healthcare NHS TrustLondon, United KingdomNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Imperial College London
- Trial code
- NCT07147725
- Record updated
- 23 September 2025
- Original description
Cardiovascular disease (CVD) is a leading global cause of morbidity and mortality and excessive healthcare expenditures. Pulmonary hypertension (PH) represents an insidious and progressive subset of CVD affecting an estimated 1% of the general population, increasing to up to 10% in the population over the age of 65. Recent advancements in artificial intelligence (AI) have shown promise in transforming PH diagnosis by enabling the analysis of complex physiological data. Specifically, AI algorithms applied to electrocardiography (ECG) and phonocardiography (PCG) waveforms captured through novel medical devices, such as smart stethoscopes, have demonstrated potential in detecting PH and other cardiovascular conditions with high sensitivity and specificity.Read full original descriptionShow less
Pulmonary Arterial Hypertension and Associated Cardiovascular Disease Detection Using Artificial Intelligence
Cardiovascular disease (CVD) is a leading global cause of morbidity and mortality and excessive healthcare expenditures. Pulmonary hypertension (PH) represents an insidious and progressive subset of CVD affecting an estimated 1% of the general population, increasing to up to 10% in the population over the age of 65. Recent advancements in artificial intelligence (AI) have shown promise in transforming PH diagnosis by enabling the analysis of complex physiological data. Specifically, AI algorithms applied to electrocardiography (ECG) and phonocardiography (PCG) waveforms captured through novel medical devices, such as smart stethoscopes, have demonstrated potential in detecting PH and other cardiovascular conditions with high sensitivity and specificity.
Despite the promising capabilities of AI algorithms, a significant barrier to their clinical implementation is the lack of high-quality, prospectively collected datasets for validation. Many existing AI algorithms have been trained on retrospective data, which may not capture the variability and complexity of real-world clinical scenarios. This limitation raises concerns about the generalisability and reliability of AI predictions across diverse patient populations.
Therefore, there is a critical need for prospective validation studies to assess the performance of AI algorithms in realworld settings, ensuring their accuracy and applicability before widespread clinical deployment. Imperial College London's Health Impact Lab (Hi Lab) and collaborators continue to develop artificial intelligence (AI) algorithms that use cardiac waveforms to predict cardiovascular disease (CVD), including pulmonary hypertension (PH). The performance of these algorithms requires validation on prospectively collected patient data (waveforms) - where the ground truth for the algorithms under investigation is recorded during routine echocardiography as part of clinical care. This study aims to prospectively collect a large dataset of cardiovascular ECG and PCG data, along with corresponding gold-standard echocardiography findings. This dataset will be used to validate AI algorithms for important CVD, such as pulmonary hypertension enhancing their reliability and clinical applicability.
Inclusion: Age 18 years or above; Able to give informed consent; Attending for echocardiography at Imperial College NHS Trust.
Exclusion: Any chest wound, skin pathology or other feature that would prohibit routine stethoscope examination.