Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918339465445376 |
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| author | Duarte, Adson Vitturini, Davide Milillo, Emanuele Bragagnolo, Andrea Barbano, Carlo Alberto Renzulli, Riccardo Cannito, Michele Giacobbe, Federico Bruno, Francesco de Filippo, Ovidio D'Ascenzo, Fabrizio Grangetto, Marco |
| author_facet | Duarte, Adson Vitturini, Davide Milillo, Emanuele Bragagnolo, Andrea Barbano, Carlo Alberto Renzulli, Riccardo Cannito, Michele Giacobbe, Federico Bruno, Francesco de Filippo, Ovidio D'Ascenzo, Fabrizio Grangetto, Marco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13846 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data Duarte, Adson Vitturini, Davide Milillo, Emanuele Bragagnolo, Andrea Barbano, Carlo Alberto Renzulli, Riccardo Cannito, Michele Giacobbe, Federico Bruno, Francesco de Filippo, Ovidio D'Ascenzo, Fabrizio Grangetto, Marco Computer Vision and Pattern Recognition 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. |
| title | Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.13846 |