Artificial Intelligence in Valvular Heart Disease: Innovations and Future Directions.
Authors
Affiliations (24)
Affiliations (24)
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom. Electronic address: https://twitter.com/AMaznyczka.
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
- Haut-Leveque Cardiology Hospital, CHU Bordeaux, Pessac, France; Cardio-Thoracic Research Centre, University of Bordeaux, Bordeaux, France.
- Institut Cardiovasculaire Paris Sud, Hôpital Privé Jacques Cartier, Ramsay Santé, Massy, France. Electronic address: https://twitter.com/DrGarot.
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands. Electronic address: https://twitter.com/markvddorpel.
- The Heart Center, Rigshospitalet, Copenhagen, Denmark. Electronic address: https://twitter.com/DrArifK.
- Second Cardiology Division, Cardiothoracic and Vascular Department, Santa Chiara University Hospital, Pisa, Italy.
- Department of Biomedical Engineering, King's College London, London, United Kingdom.
- The Blalock Taussig Thomas Heart Center, John Hopkins Hospital and School of Medicine, Baltimore, Maryland, USA. Electronic address: https://twitter.com/ShelbyKuttyMD.
- Brigham and Women's Hospital, Boston, Massachusetts, USA. Electronic address: https://twitter.com/jgrapsa.
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Switzerland. Electronic address: https://twitter.com/chrisgraeni.
- Division of Cardiology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas USA.
- Department of Biomedical Sciences, The Ohio State University College of Medicine, Columbus, Ohio, USA.
- Cardiovascular Department, King's College Hospital NHS Foundation Trust London, London, United Kingdom; School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, United Kingdom.
- Feops-Materialise (FEops), Ghent, Belgium.
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- The Heart Center, Rigshospitalet, Copenhagen, Denmark.
- Department of Cardiology, Angiology and Pneumonology, Heidelberg University Hospital, Heidelberg, Germany/German Centre for Cardiovascular Research (DZHK), Partner Site Heidelberg-Mannheim, Germany.
- Heart Clinic Hirslanden, Zurich, Switzerland/University of Zurich, Zurich, Switzerland.
- Division of Cardiology, Department of Medicine, Northwestern University, Feinberg School of Medicine, Chicago, Illinois, USA. Electronic address: https://twitter.com/FarazAhmadMD.
- Center for Structural Heart Disease, Henry Ford Health System, Detroit, Michigan, USA. Electronic address: https://twitter.com/DeeDeeWangMD.
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA. Electronic address: https://twitter.com/hahn_rt.
- The Heart Center, Rigshospitalet, Copenhagen, Denmark. Electronic address: [email protected].
Abstract
Managing valvular heart disease (VHD) requires integrating multimodal data, including demographics, symptoms, biomarkers, electrocardiogram findings, and imaging studies. However, the capacity and processing power of the human mind are limited, particularly in the current era where vast quantities of complex data require rapid processing. Integrating artificial intelligence (AI) into the management of VHD offers an opportunity to enhance diagnostic accuracy, streamline clinical workflows, optimize procedural strategies, and predict outcomes and disease progression. Subsets of AI such as machine learning and deep learning algorithms can uncover the unseen data from routine investigations (eg, electrocardiograms, echocardiography, and computed tomography), providing robust and accurate risk prediction tools to inform personalized treatment strategies. Intraprocedurally, AI-based enhancements in imaging guidance can be leveraged to improve procedural safety and success. Digital twin technology can allow case-specific disease modelling, such as simulating valve designs and predicting adverse events, fostering precision medicine. By using the full potential of AI, clinicians can provide a comprehensive, personalized management strategy for VHD patients, ultimately enhancing clinical outcomes. However, models based on AI algorithms require rigorous validation across multiple centers to ensure their reliability. Concerns about bias, data privacy, and limited transparency challenge the application of AI decision-making to digital health care. This review discusses the applications of AI in the management of patients with VHD, highlights the future directions of AI technologies, and considers the challenges of integrating AI into clinical practice.