Pulmonary Arterial Hypertension and Associated Cardiovascular Disease Detection Using Artificial Intelligence

Summary

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.

Conditions

• Pulmonary Hypertension
• Cardiovascular Diseases (CVD)

Interventions

DEVICE

AI Stethoscope

Patients attending routine echocardiography who satisfy the inclusion and exclusion criteria will be approached before their echocardiography appointment to obtain informed consent to participate in the study. On providing informed consent, each patient will receive a non-invasive, external examination with a smart stethoscope that records a 3-lead electrocardiogram (ECG) and phonocardiogram (PCG) waveforms. This examination will require only one study visit (during routine echocardiography) and no additional visits. The stethoscope is a fully CE-marked device. In addition to echocardiography parameters and smart stethoscope waveforms, baseline demographics, clinical and medication history will be recorded. These data points will be re-examined at 24 months following enrolment (via chart review).

Sponsors & Collaborators

• Imperial College Healthcare NHS Trust

  collaborator OTHER

• Imperial College London

  lead OTHER

Principal Investigators

• Nicholas S Peters, MD · Imperial College London

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2025-10-01

Primary Completion

2025-12-01

Completion

2027-08-01

Countries

• United Kingdom

Study Locations