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 EchoJEPA foundation model, pretrained on more than 18 million ultrasound recordings, with modified output layers for automatic echocardiographic view classification, providing a baseline component for a larger automated pipeline for HF diagnosis from echocardiography video/image data. This work was presented at the 5th Serbian International Conference on Applied Artificial Intelligence (SICAAI 2026), Kragujevac, Serbia, and was carried out within the STRATIFYHF project.
Heart failure (HF) affects over 64.3 million people worldwide. As a part of the StratifyHF project, we developed a decision support system (DSS) to enhance HF prediction and diagnosis through machine learning (ML) approaches. The DSS comprises two modules: Early diagnosis and Ris…
Distinguishing heart failure with reduced ejection fraction (HFrEF) from heart failure with preserved ejection fraction (HFpEF) is clinically important but challenging. This paper presents an end-to-end deep learning pipeline for automated three-class classification (healthy, HFr…
Heart failure is one of the most life-threatening diseases of the modern era, with high global mortality and morbidity rates, motivating the need for long-term outcome prediction. One established tool is the MAGGIC Risk Calculator for Heart Failure, which predicts 3-5 year mortal…
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…
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…
Heart failure and chronic obstructive pulmonary disease often present with overlapping clinical signs, making differential diagnosis challenging. This work applies machine learning, primarily random forest ensembles trained on heterogeneous clinical data (physical examination, bl…