ArticleFrontiers in cardiovascular medicine2023
Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Preoperative global longitudinal strain predicts myocardial hypertrophy burden but not the magnitude of reverse remodeling after surgical aortic valve replacement in severe aortic stenosis.Journal of cardiovascular imaging · 2026Article
- Predicting Hypertension Persistence in Coarctation of the Aorta: A Feasibility Study.Annals of biomedical engineering · 2026Article
- Transcatheter Aortic Valve Replacement in Older Adults: Integrating Cardiac Remodeling and Geriatric Syndromes-A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges.Diagnostics (Basel, Switzerland) · 2024Review
- CardioVision: A fully automated deep learning package for medical image segmentation and reconstruction generating digital twins for patients with aortic stenosis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2023Article
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Authors and funding
10 authors.
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Abstract
Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and
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