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 cardi