AI integration in valvular heart disease imaging offers a scalable model to improve diagnosis, procedural planning, and personalized therapy from community screening to tertiary care.
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.
Sengupta et al. (Fri,) studied this question.