Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916555079548928 |
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| author | Turgut, Özgün Müller, Philip Hager, Paul Shit, Suprosanna Starck, Sophie Menten, Martin J. Martens, Eimo Rueckert, Daniel |
| author_facet | Turgut, Özgün Müller, Philip Hager, Paul Shit, Suprosanna Starck, Sophie Menten, Martin J. Martens, Eimo Rueckert, Daniel |
| contents | Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardiogram (ECG) is a cost-effective and widely available diagnostic aid that provides functional information of the heart. However, its ability to classify and spatially localise CVD is limited. In contrast, cardiac magnetic resonance (CMR) imaging provides detailed structural information of the heart and thus enables evidence-based diagnosis of CVD, but long scan times and high costs limit its use in clinical routine. In this work, we present a deep learning strategy for cost-effective and comprehensive cardiac screening solely from ECG. Our approach combines multimodal contrastive learning with masked data modelling to transfer domain-specific information from CMR imaging to ECG representations. In extensive experiments using data from 40,044 UK Biobank subjects, we demonstrate the utility and generalisability of our method for subject-specific risk prediction of CVD and the prediction of cardiac phenotypes using only ECG data. Specifically, our novel multimodal pre-training paradigm improves performance by up to 12.19 % for risk prediction and 27.59 % for phenotype prediction. In a qualitative analysis, we demonstrate that our learned ECG representations incorporate information from CMR image regions of interest. Our entire pipeline is publicly available at https://github.com/oetu/MMCL-ECG-CMR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_05764 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging Turgut, Özgün Müller, Philip Hager, Paul Shit, Suprosanna Starck, Sophie Menten, Martin J. Martens, Eimo Rueckert, Daniel Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardiogram (ECG) is a cost-effective and widely available diagnostic aid that provides functional information of the heart. However, its ability to classify and spatially localise CVD is limited. In contrast, cardiac magnetic resonance (CMR) imaging provides detailed structural information of the heart and thus enables evidence-based diagnosis of CVD, but long scan times and high costs limit its use in clinical routine. In this work, we present a deep learning strategy for cost-effective and comprehensive cardiac screening solely from ECG. Our approach combines multimodal contrastive learning with masked data modelling to transfer domain-specific information from CMR imaging to ECG representations. In extensive experiments using data from 40,044 UK Biobank subjects, we demonstrate the utility and generalisability of our method for subject-specific risk prediction of CVD and the prediction of cardiac phenotypes using only ECG data. Specifically, our novel multimodal pre-training paradigm improves performance by up to 12.19 % for risk prediction and 27.59 % for phenotype prediction. In a qualitative analysis, we demonstrate that our learned ECG representations incorporate information from CMR image regions of interest. Our entire pipeline is publicly available at https://github.com/oetu/MMCL-ECG-CMR. |
| title | Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging |
| topic | Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2308.05764 |