Towards patient-specific prediction of conduction abnormalities induced by transcatheter aortic valve implantation: a combined mechanistic modelling and machine learning approach.
Valeria GalliFilip LoncaricGiorgia RocatelloPatricio AstudilloLaura SanchisAnder RegueiroOle De BackerMartin SwaansJohan BosmansJoana Maria RibeiroPablo LamataMarta SitgesPeter de JaegerePeter MortierPublished in: European heart journal. Digital health (2021)
ML, integrating statistical and mechanistic modelling, achieved an accurate prediction of CA after TAVI. This study demonstrates the potential of a synergetic approach for personalizing procedure planning, allowing selection of the optimal device and implantation strategy, avoiding new CA and/or PPI.
Keyphrases
- transcatheter aortic valve implantation
- aortic valve
- aortic valve replacement
- aortic stenosis
- machine learning
- transcatheter aortic valve replacement
- ejection fraction
- high resolution
- artificial intelligence
- protein kinase
- minimally invasive
- left ventricular
- risk assessment
- human health
- small molecule
- coronary artery disease
- protein protein
- atrial fibrillation