Revolutionizing early detection of heart failure through point-of-care echocardiography enhanced by AI
Heart failure remains a leading cause of morbidity and mortality worldwide, with early detection being pivotal for improving outcomes. Our project proposes a groundbreaking approach to revolutionize early detection of heart failure through an innovative integration of point-of-care echocardiography, accessible echocardiography with a portable device, and artificial intelligence (AI). This initiative aims to democratize advanced cardiac care, making it accessible and efficient for patients everywhere.
The core of our project is the deployment of a portable, easy-to-use echocardiographic device that utilizes technology to facilitate rapid, accurate assessments of cardiac function at the point of care. This tool is designed for widespread use, from primary care settings to remote locations, ensuring that no patient is left behind in the fight against heart failure.
A cornerstone of our approach is the application of AI in analyzing the left ventricular ejection fraction, a key parameter in diagnosing heart failure. This method not only enhances diagnostic accuracy but also significantly reduces the time and expertise required to interpret echocardiographic results.
Importance of different imaging modalities
Although our research group mainly focuses on research projects revolving around CT imaging, we do collaborate with, and utilize, other imaging modalities such as MRI, PET-CT, and echocardiography for extensive research projects.
The primary aim in the MERMAID-HF project – a sub-study integrated in DANE-HEART – is to assess a targeted screening strategy to detect undiagnosed heart failure (HF) in high-risk patients.
Participants will be recruited from five countries (Denmark, Canada, USA, Sweden and Scotland). Individual patient data from similar national randomised controlled trials that are independently powered for different efficacy endpoints will be pooled, harmonised and analysed.
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