The progression of cardiovascular disease (CVD) and its ultimate clinical outcomes are influenced by multiple factors, including genetics, lifestyle, environment, and comorbidities as well as patient-specific anatomy and physiology. The diagnosis, prognosis, and treatment of CVD thus require a comprehensive and personalized approach that considers the individual characteristics and needs of each patient. Digital twins, which aim to simulate one or more aspects of patient-specific (patho)physiology, are emerging as a powerful tool to enable such an approach. In the context of CVD, digital twins can integrate multiple sources of data, such as in-patient telemetry, medical imaging, electrocardiograms, blood tests, wearable sensors, and genomic information, to create detailed and dynamic models of a patient’s functional physiology (electrophysiology, circulation, oxygenation), heart, heart valve(s), or blood vessel(s). These models—mechanistic, data-driven, or likely some combination thereof—can provide insights into the underlying mechanisms and risk factors of CVD, communicate early warning signs of deteriorating CVD to the clinical team, as well as predict the outcomes and effects of possible interventions.
Digital twins promise to help clinicians to diagnose CVD more accurately, to monitor CVD progression and post-therapeutic response more effectively, and to optimize CVD management and prevention more efficiently. Digital twins can also facilitate cardiovascular research and education by enabling the exploration of complex scenarios and the generation of new hypotheses. The purpose of this Research Topic is to provide a comprehensive overview of the current state-of-the-art and future directions of digital twin technology for CVD. We also aim to stimulate further research and innovation in this exciting and promising field.
We invite original research articles, reviews, perspectives, and commentaries that cover topics such as:
• The design and implementation of digital twin models for different types of CVD
• The validation and evaluation of digital twin models in pre-clinical or clinical settings
• The integration and analysis of heterogeneous data sources for digital twin models
• The development and application of data-driven methods, including artificial intelligence/machine learning approaches, for digital twin models
• The challenges in building and implementing digital twins for regular clinical use
• The ethical, legal, and social implications of digital twin technology for cardiovascular medicine
Keywords:
Digital twin, cardiovascular, modeling, simulation, prediction
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
The progression of cardiovascular disease (CVD) and its ultimate clinical outcomes are influenced by multiple factors, including genetics, lifestyle, environment, and comorbidities as well as patient-specific anatomy and physiology. The diagnosis, prognosis, and treatment of CVD thus require a comprehensive and personalized approach that considers the individual characteristics and needs of each patient. Digital twins, which aim to simulate one or more aspects of patient-specific (patho)physiology, are emerging as a powerful tool to enable such an approach. In the context of CVD, digital twins can integrate multiple sources of data, such as in-patient telemetry, medical imaging, electrocardiograms, blood tests, wearable sensors, and genomic information, to create detailed and dynamic models of a patient’s functional physiology (electrophysiology, circulation, oxygenation), heart, heart valve(s), or blood vessel(s). These models—mechanistic, data-driven, or likely some combination thereof—can provide insights into the underlying mechanisms and risk factors of CVD, communicate early warning signs of deteriorating CVD to the clinical team, as well as predict the outcomes and effects of possible interventions.
Digital twins promise to help clinicians to diagnose CVD more accurately, to monitor CVD progression and post-therapeutic response more effectively, and to optimize CVD management and prevention more efficiently. Digital twins can also facilitate cardiovascular research and education by enabling the exploration of complex scenarios and the generation of new hypotheses. The purpose of this Research Topic is to provide a comprehensive overview of the current state-of-the-art and future directions of digital twin technology for CVD. We also aim to stimulate further research and innovation in this exciting and promising field.
We invite original research articles, reviews, perspectives, and commentaries that cover topics such as:
• The design and implementation of digital twin models for different types of CVD
• The validation and evaluation of digital twin models in pre-clinical or clinical settings
• The integration and analysis of heterogeneous data sources for digital twin models
• The development and application of data-driven methods, including artificial intelligence/machine learning approaches, for digital twin models
• The challenges in building and implementing digital twins for regular clinical use
• The ethical, legal, and social implications of digital twin technology for cardiovascular medicine
Keywords:
Digital twin, cardiovascular, modeling, simulation, prediction
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.