A Modular Deep Learning Pipeline for Echocardiographic Video Classification of Heart Failure
Tijana Geroski, Mahyar Bolhassani, Narayana S. Singam, Ayman Battisha, Nenad Filipović, Dinesh Kalra, Amir A. Amini
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, HFrEF, HFpEF) from 2D apical four-chamber echocardiographic videos, combining the public RVENet dataset with institutional data from the University of Louisville. After a multi-step preprocessing pipeline (colour-space conversion, cardiac-cycle detection, motion-based cropping, normalization), a lightweight 3D SqueezeNet and a deeper ResNet50 were evaluated as spatio-temporal classifiers, achieving AUC values of 0.93-0.98 across classes, with ResNet50 reaching an F1-score of up to 0.9205 for HFrEF when using two cardiac cycles as input. This work was presented at SPIE Medical Imaging 2026: Clinical and Biomedical Imaging, Vancouver, BC, Canada, and was carried out in part within the STRATIFYHF project.