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.

翻译：心输出量（CO）是心血管疾病诊断与管理的关键参数。然而，其精确测量需要右心导管插入术——一种侵入性且耗时的操作，这推动了利用超声心动图开发可靠无创替代方法的研究。本研究提出一种基于SimCLR的自监督学习预训练策略，旨在提升通过心尖四腔心超声心动图视频预测心输出量的性能。预训练使用与下游任务相同的有限数据集进行，证明了即使在数据稀缺条件下自监督学习仍具潜力。实验结果表明，自监督学习能有效缓解过拟合并提升表征学习能力，在测试集上达到0.41的平均皮尔逊相关系数，其性能优于基于超百万次超声心动图检查训练的PanEcho模型。源代码发布于https://github.com/EIDOSLAB/cardiac-output。