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 LachmannElena RippenDaniel RuckertTibor SchusterErion XhepaMoritz von ScheidtCostanza PellegriniTeresa TrenkwalderTobias RheudeAnja StundlRuth ThalmannGerhard HarmsenShinsuke YuasaHeribert SchunkertAdnan KastratiMichael JonerChristian KupattKarl-Ludwig LaugwitzPublished in: European heart journal. Digital health (2022)
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.
Keyphrases
- aortic stenosis
- aortic valve
- convolutional neural network
- transcatheter aortic valve replacement
- deep learning
- aortic valve replacement
- machine learning
- transcatheter aortic valve implantation
- big data
- left ventricular
- artificial intelligence
- electronic health record
- early onset
- resistance training
- blood flow
- ejection fraction
- data analysis
- acute coronary syndrome
- acute myocardial infarction
- percutaneous coronary intervention
- coronary artery disease
- pulmonary arterial hypertension
- high intensity