Mij
Senior Member (Voting Rights)
Autonomic disorders, especially those characterized by orthostatic intolerance such as Postural Tachycardia Syndrome (POTS), remain diagnostically and therapeutically challenging due to their complex pathophysiology and limited access to specialized care.
This paper proposes a conceptual framework for applying digital twin technology to POTS and other autonomic disorders. A digital autonomic twin—a dynamic, virtual replica of a patient’s autonomic system—offers a transformative approach to understanding, predicting, and managing these conditions. A dynamic digital twin framework integrates mechanistic and AI-based modeling utilizing continuous physiological, clinical, genetic, and patient-reported data to enhance individualized diagnosis, disease monitoring, and treatment.
This system can simulate autonomic responses, predict disease trajectories, and personalize interventions. Digital twins provide real-time physiological modeling, adaptive treatment simulations, lifestyle intervention tracking, and integration of environmental and biometric data. Key components include wearable devices, electronic health records, AI-driven simulations, and clinician interfaces. However, challenges such as data volume, model transparency, and ethical considerations must be addressed.
In conclusion, digital twin technology has the potential to revolutionize the management of POTS and related autonomic disorders, transitioning to personalized, predictive, adaptive medicine by providing a continuously updated and tailored approach to neurological care.
LINK
This paper proposes a conceptual framework for applying digital twin technology to POTS and other autonomic disorders. A digital autonomic twin—a dynamic, virtual replica of a patient’s autonomic system—offers a transformative approach to understanding, predicting, and managing these conditions. A dynamic digital twin framework integrates mechanistic and AI-based modeling utilizing continuous physiological, clinical, genetic, and patient-reported data to enhance individualized diagnosis, disease monitoring, and treatment.
This system can simulate autonomic responses, predict disease trajectories, and personalize interventions. Digital twins provide real-time physiological modeling, adaptive treatment simulations, lifestyle intervention tracking, and integration of environmental and biometric data. Key components include wearable devices, electronic health records, AI-driven simulations, and clinician interfaces. However, challenges such as data volume, model transparency, and ethical considerations must be addressed.
In conclusion, digital twin technology has the potential to revolutionize the management of POTS and related autonomic disorders, transitioning to personalized, predictive, adaptive medicine by providing a continuously updated and tailored approach to neurological care.
LINK