A Digital Twin Framework for Human Organs modeling and forecasting in the Metaverse

Digital Twins (DTs) have emerged as essential tools for representing physical systems and their interactions within virtual environments. In healthcare, DTs enable new possibilities for real-time visualization, monitoring, and prediction of physiological and biological processes. Integrating the Metaverse with DTs redefines human-computer interaction by seamlessly blending physical and virtual reality. In this paper, we propose an immersive digital twin framework for human organs that integrates real-time bio-signal data. We design a DT framework by creating 3D models of the heart and lungs organs in Unity, targeting Virtual Reality (VR) platforms, such as the Meta Quest. These models animate dynamically in response to live sensor streams (e.g., smartwatchderived heart rate). Additionally, we enhance the framework with machine learning models that forecast short-term physiological trends and can also detect anomalies in the patterns. The proposed system supports real-time interaction, visualization, and prediction, offering applications in remote healthcare, continuous monitoring, medical training, and patient education. Our approach demonstrates the potential of combining immersive environments with bio-signal-driven machine learning to create intelligent, interactive, and adaptive healthcare systems with application sin surgery and medicine, in general.