E. Rippen

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

T. Schuster, H. Schunkert, M. Joner, 2021, JACC. Cardiovascular interventions.

Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement

H. Schunkert, M. Joner, A. Kastrati, 2022, Open Heart.

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

M. Joner, S. Yuasa, K. Laugwitz, 2022, JACC. Cardiovascular interventions.

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

D. Rueckert, H. Schunkert, M. Joner, 2022, European heart journal. Digital health.

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

H. Schunkert, M. Joner, A. Kastrati, 2023, European heart journal. Cardiovascular Imaging.

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

M. Joner, K. Laugwitz, T. Rheude, 2024, Structural heart : the journal of the Heart Team.