ExWAS Results: Deep Learning Cardiac MRI Phenotypes for Aortic Valve Dysfunction

This dataset contains results from an Exposome-Wide Association Study (ExWAS) linking lifestyle and clinical factors to early aortic valve dysfunction. Deep learning-derived cardiac MRI phenotypes serve as the primary data, connecting exposures to cardiovascular outcomes. The analysis is based on a dataset of 155,645 entries, shared under an open license.

Use Cases

• Identifying lifestyle risk factors for aortic valve dysfunction based on deep learning phenotypes.
• Conducting an exposome-wide association study (ExWAS) using cardiac MRI data.
• Validating deep learning models for extracting cardiac phenotypes from medical images.
• Exploring connections between clinical measurements and early cardiovascular disease markers.

Strengths

• Dataset size is explicitly stated as 155,645 entries.
• Released under a permissive CC-BY-4.0 license, facilitating reuse.

Limitations

• Column names and a precise row count are not provided, limiting understanding of data structure.
• The 'Last updated' date of 2026-03-21 is in the future, indicating a metadata error or placeholder.

Provenance

Source

Wang Tingting

Collection Method

Likely derived from a research study applying deep learning to cardiac MRI data for an ExWAS.

Time Range

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Freshness

Last updated metadata is listed as 2026-03-21, which is inconsistent.

Geography

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