ArticleFrontiers in cardiovascular medicine2026
A machine learning predictive model based on conventional two-dimensional echocardiography and serum biomarkers for early detection of ascending aorta dilation in BAV patients.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- From Valve Anatomy to Molecular Trajectories: Integrating Proteomics into Precision Care for Bicuspid Aortic Valve Disease.Journal of cardiovascular development and disease · 2026Review
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6 authors.
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Abstract
Objective: In order to address the challenge of early detection of ascending aortic dilation (AAD) in patients with bicuspid aortic valve (BAV), a machine learning prediction model integrating ultrasound hemodynamics and serum markers was developed to break through the limitations of traditional anatomical indicators. Methods: A total of 51 patients with BAV were prospectively enrolled and divided into ascending aortic dilation group (BAV-D, Results: AAoV, AAoMPG and HDL-C in the BAV-D group were significantly higher than those in the BAV-ND group (all Conclusion: The machine learning model constructed by integrating hemodynamics (AAoV) and metabolic markers (HDL-C and ApoB) for the first time can accurately quantify the risk of AAD in BAV patients, and its performance is significantly better than that of a single anatomical parameter, providing a visual decision-making tool for early intervention.
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