Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact

• Published in: Journal of the American Heart Association 2024-10, Vol.13 (19), p.e031981
• Main Authors: Sel, Kaan, Osman, Deen, Zare, Fatemeh, Masoumi Shahrbabak, Sina, Brattain, Laura, Hahn, Jin-Oh, Inan, Omer T, Mukkamala, Ramakrishna, Palmer, Jeffrey, Paydarfar, David, Pettigrew, Roderic I, Quyyumi, Arshed A, Telfer, Brian, Jafari, Roozbeh
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - physiopathology; Cardiovascular Diseases - therapy; cardiovascular modeling; computational physiology; digital representation; Humans; precision health; Precision Medicine - methods
• ISSN: 2047-9980; 2047-9980
• DOI: 10.1161/JAHA.123.031981

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.