ArticleEuropean heart journal. Case reports2025
Artificial intelligence in the diagnosis and management planning of bicuspid aortic valvular disease: a case series.
Article in European heart journal. Case reports, 2025. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Bicuspid aortic valve (BAV) is the most common congenital heart anomaly, often leading to significant aortic stenosis (AS) or aortic regurgitation (AR), which may require surgical intervention. Echocardiography is typically used for the diagnosis of BAV, and the integration of artificial intelligence (AI) can enhance diagnostic accuracy and guide surgical decisions. Case summary: We present two patients with BAV: a 17-year-old male football player with isolated AR due to prolapse undergoing aortic valve repair and a 68-year-old male with combined AS and AR, candidate for aortic valve replacement. Artificial intelligence-based tools assisted in characterizing the valvular disease and assessing its haemodynamic impact by estimating and averaging transvalvular gradients and velocity-time integrals, reconstructing three-dimensional valve anatomy, and automatically calculating left ventricular volumes, ejection fraction, and global longitudinal strain. This comprehensive assessment improved prognostic evaluation and helped tailor the treatment plan. Conclusion: Artificial intelligence in echocardiography holds great potential for diagnosis and planning the treatment of BAV disease. By enhancing image analysis and automating key diagnostic steps, AI can reduce diagnostic times and optimize patient outcomes. As AI-based tools continue to evolve and gain clinical validation, their integration into everyday practice will likely lead to a more efficient and accurate care for patients with valvular heart disease.
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