In its most straightforward definition, a digital twin is a virtual representation of a physical object, but its implementation is often more intricate. In healthcare, a digital twin can extend to represent populations or even specific organs, like the human heart. What sets a digital twin apart from a simple 3D model is its ability to emulate the behavior of the physical entity it represents.
Natalia Trayanova, a professor at Johns Hopkins University, describes a digital twin as a model that incorporates all the components of an entity and their dynamic interactions. It goes beyond mere geometry representation; it must illustrate how the various components interact with each other in real-time.
Digital twins have found applications in healthcare, contributing to predictive modeling for both physiological and sociological behaviors. One notable application is in the treatment of heart rhythm disorders. Trayanova’s team at Johns Hopkins creates personalized digital twins based on a patient’s heart geometry, including structural details, disease-related remodeling identified through imaging scans, and electrical wave propagation.
When an electrical wave triggers a contraction in the heart, any abnormalities, such as scarring or damage, can lead to arrhythmias. The digital twin allows researchers to send signals and observe how electrical waves propagate through the model, helping predict potential arrhythmias in patients.
The predictive power of digital twins extends to treatment decisions. For example, in treating arrhythmias, catheter ablation is a common approach. Digital twins assist physicians in determining the optimal locations for tissue destruction. By examining the twin’s behavior, physicians can precisely place the catheter for effective treatment.
Johns Hopkins employs high-performance computing systems and neural networks to run digital twin simulations, demonstrating the integration of artificial intelligence and machine learning in the process. This approach has led to the approval of the first digital twin in cardiology by the Food and Drug Administration and its use in a randomized clinical trial.
The foundation of digital twin technology lies in data, sourced from various sources such as sensors, scans, medical records, and insurance records. High-quality 3D images and animations further enhance the accuracy of the representation. AWS IoT TwinMaker is a tool that enables medical companies to combine data sets and 3D images to quickly create operational twins. It facilitates the overlay of contextual information onto the twin, providing a deeper understanding of the environment and influencing factors.
Digital twin technology is not exclusive to advanced cloud adopters; even those in the early stages of their cloud journey can leverage tools like AWS IoT TwinMaker to create and benefit from digital twins in healthcare. The ability to analyze large, robust data sets is crucial for training AI models and enhancing the accuracy of simulations in digital twin applications.
