Current and future use of artificial intelligence in valvular heart disease imaging.
Authors
Affiliations (9)
Affiliations (9)
- Division of Cardiovascular Diseases and Hypertension, Rutgers Robert Wood Johnson Medical School, 125 Patterson St, New Brunswick, NJ 08901, USA.
- Cardiovascular Services, Robert Wood Johnson University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA.
- Department of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
- MIRACL.ai Laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence (AP-HP), Université Paris Cité, Paris, France.
- Department of Cardiology, University Hospital of Lariboisiere (AP-HP), Paris, France.
- Université Paris Cité, Inserm MASCOT-UMRS 942, 75010  Paris, France.
- Department of Medicine, University of British Columbia and Vancouver General Hospital Artificial Intelligence Echocardiography Core Laboratory, Vancouver, BC, Canada V5Z 1M9.
- Division of Cardiology, University of British Columbia, Vancouver, BC, Canada V6T 1Z4.
- Department of Cardiology, Centrum Voor Hart-en Vaatziekten, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Laarbeeklaan 101, Brussels 1090, Belgium.
Abstract
Valvular heart disease (VHD) remains significantly underdiagnosed and undertreated. This review examines an artificial intelligence (AI)-enhanced 'spoke-hub-node' care model designed to improve the early detection, risk stratification, and treatment of VHD. In this model, AI tools-such as automated ECG interpretation, digital stethoscopes, and point-of-care ultrasound-facilitate decentralized screening and referral for cardiac imaging at the community level. During the transition from outpatient settings to tertiary care centres, AI-integrated echocardiography, cardiac tomography, and magnetic resonance imaging facilitate advanced diagnostic evaluation and inform procedural planning. We review emerging innovations that can enhance this model of care delivery-including unsupervised machine learning to uncover novel VHD phenotypes, generative AI for automated reporting, the use of digital twins to simulate interventions, and the integration of multiple AI agents to support heart team meetings. These advances are followed by the emerging use of AI in robotic transoesophageal and intracardiac echocardiography, as well as in fusion fluoroscopy imaging, to guide valve interventions. While outlining the challenges inherent in this rapidly evolving field, the review's central contribution is its vision to connect the continuum-from AI-enabled community screening to personalized, image-guided therapies at tertiary care centres-offering a scalable and equitable model for VHD care.