Elena Rippen

Subphenotyping of Patients With Aortic Stenosis by Unsupervised Agglomerative Clustering of Echocardiographic and Hemodynamic Data

Mark Lachmann, Elena Rippen, Tibor Schuster et al. · JACC: Cardiovascular Interventions · 2021 · 42 citations

Heart Failure, Cardiovascular Disease, Unsupervised Agglomerative Clustering +12

Solving the Pulmonary Hypertension Paradox in Patients With Severe Tricuspid Regurgitation by Employing Artificial Intelligence

Vera Fortmeier, Mark Lachmann, Maria Isabel Körber et al. · JACC: Cardiovascular Interventions · 2022 · 28 citations

Artificial Intelligence, Pulmonary Hypertension Paradox, Engineering +10

Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair

Teresa Trenkwalder, Mark Lachmann, Lukas Stolz et al. · European Heart Journal - Cardiovascular Imaging · 2023 · 18 citations

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

Mark Lachmann, Elena Rippen, Daniel Rueckert et al. · European Heart Journal - Digital Health · 2022 · 17 citations · Full text

Deep Learning–Enabled Assessment of Right Ventricular Function Improves Prognostication After Transcatheter Edge-to-Edge Repair for Mitral Regurgitation

Mark Lachmann, Vera Fortmeier, Lukas Stolz et al. · Circulation Cardiovascular Imaging · 2025 · 10 citations