Thoracic Aortic Three-Dimensional Geometry
Abstract
BackgroundAortic structure impacts cardiovascular health through multiple mechanisms. Aortic structural degeneration occurs with aging, increasing left ventricular afterload and promoting increased arterial pulsatility and target organ damage. Despite the impact of aortic structure on cardiovascular health, three-dimensional (3D) aortic geometry has not been comprehensively characterized in large populations.
MethodsWe segmented the complete thoracic aorta using a deep learning architecture and used morphological image operations to extract multiple aortic geometric phenotypes (AGPs, including diameter, length, curvature, and tortuosity) across various subsegments of the thoracic aorta. We deployed our segmentation approach on imaging scans from 54,241 participants in the UK Biobank and 8,456 participants in the Penn Medicine Biobank.
ConclusionOur method provides a fully automated approach towards quantifying the three-dimensional structural parameters of the aorta. This approach expands the available phenotypes in two large representative biobanks and will allow large-scale studies to elucidate the biology and clinical consequences of aortic degeneration related to aging and disease states.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper is relevant to longevity research as it addresses the structural degeneration of the aorta, which is a significant factor in cardiovascular health and aging. By characterizing aortic geometry in large populations, it contributes to understanding the biological processes associated with aging and potential interventions. However, while the methodology is innovative, the findings are more of a solid research contribution rather than a groundbreaking advance, hence the moderate impact score.
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