Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires right-heart catheterization, an invasive and time-consuming procedure, motivating the development of reliable non-invasive alternatives using echocardiography. In this work, we propose a self-supervised learning (SSL) pretraining strategy based on SimCLR to improve CO prediction from apical four-chamber echocardiographic videos. The pretraining is performed using the same limited dataset available for the downstream task, demonstrating the potential of SSL even under data scarcity. Our results show that SSL mitigates overfitting and improves representation learning, achieving an average Pearson correlation of 0.41 on the test set and outperforming PanEcho, a model trained on over one million echocardiographic exams. Source code is available at https://github.com/EIDOSLAB/cardiac-output.
@article{arxiv.2602.13846,
title = {Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data},
author = {Adson Duarte and Davide Vitturini and Emanuele Milillo and Andrea Bragagnolo and Carlo Alberto Barbano and Riccardo Renzulli and Michele Cannito and Federico Giacobbe and Francesco Bruno and Ovidio de Filippo and Fabrizio D'Ascenzo and Marco Grangetto},
journal= {arXiv preprint arXiv:2602.13846},
year = {2026}
}