ArticleEuropean heart journal. Digital health2022
Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis.
Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 17 citations in OpenAlex.
- Applications of Artificial Intelligence in Transcatheter Aortic Valve Replacement: A Review of the Literature.Life (Basel, Switzerland) · 2025Review
- Phenotyping valvular heart diseases using the lens of unsupervised machine learning: a scoping review.NPJ cardiovascular health · 2025Article
- Invasive Assessment of Right Ventricular to Pulmonary Artery Coupling Improves 1-year Mortality Prediction After Transcatheter Aortic Valve Replacement and Anticipates the Persistence of Extra-Aortic Valve Cardiac Damage.Structural heart : the journal of the Heart Team · 2024Article
- Outcomes and predictors of left ventricle recovery in patients with severe left ventricular dysfunction undergoing transcatheter aortic valve implantation.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2024Article
- Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.Journal of clinical medicine · 2023Article
- Epiphenomenon or Prognostically Relevant Interventional Target? A Novel Proportionality Framework for Severe Tricuspid Regurgitation.Journal of the American Heart Association · 2023Article
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Authors and funding
18 authors at 6 institutions in 5 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Aims: Hypothesizing that aortic outflow velocity profiles contain more valuable information about aortic valve obstruction and left ventricular contractility than can be captured by the human eye, features of the complex geometry of Doppler tracings from patients with severe aortic stenosis (AS) were extracted by a convolutional neural network (CNN). Methods and results: After pre-training a CNN (VGG-16) on a large data set (ImageNet data set; 14 million images belonging to 1000 classes), the convolutional part was employed to transform Doppler tracings to 1D arrays. Among 366 eligible patients [age: 79.8 ± 6.77 years; 146 (39.9%) women] with pre-procedural echocardiography and right heart catheterization prior to transcatheter aortic valve replacement (TAVR), good quality Doppler tracings from 101 patients were analysed. The convolutional part of the pre-trained VGG-16 model in conjunction with principal component analysis and k-means clustering distinguished two shapes of aortic outflow velocity profiles. Kaplan-Meier analysis revealed that mortality in patients from Cluster 2 ( Conclusion: Transfer learning enables sophisticated pattern recognition even in clinical data sets of limited size. Importantly, it is the left ventricular compensation capacity in the face of increased afterload, and not so much the actual obstruction of the aortic valve, that determines fate after TAVR.
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