Voice characteristics are an emerging, non-invasive biomarker for heart failure. This study develops a machine learning pipeline to differentiate patients with suspected heart failure from those with a confirmed diagnosis using vocal features alone, drawing on 240 patients (50 su…
Accurate classification of echocardiographic views (e.g. apical 2- and 4-chamber, parasternal long-axis) is an important prerequisite for reliable ejection-fraction assessment and heart failure diagnosis, but manual classification is time-consuming. This study evaluates the EchoJ…
The STRATIFYHF project develops an AI-based decision support system integrating patient data with computational modelling to support risk stratification, diagnosis and progression prediction in heart failure. This study evaluates the cost-effectiveness of AI-enhanced ECG screenin…