Digital Twin Initiative

Healthcare is shifting from reactive treatment to proactive, predictive care. Digital twins—dynamic, data-driven representations of individuals that integrate real-time data, computation, and modeling—enable earlier detection, personalized intervention, and continuous monitoring.

However, the underlying data and tools remain fragmented. Imaging, wearable data, clinical records, behavioral signals, and computational models are developed and analyzed in isolation, limiting their ability to capture the full trajectory of human health.

The Duke Center for Computational and Digital Health Innovation (CCDHI) is actively bringing these pieces together. By integrating expertise across medicine, engineering, high performance computing, artificial intelligence, imaging, and digital health, CCDHI is establishing a cohesive and scalable foundation for digital twin science.

A defining strength of this effort is Duke’s leadership in advanced imaging. Quantitative MRI, functional imaging, and large-scale image analysis provide high-resolution insight into structure and physiology—forming the basis for accurate, mechanistic digital twins.

The Initiative

The Duke Digital Twin Initiative, led by CCDHI, is building an integrated platform that brings together multimodal data, physics-based modeling, and AI to enable human digital twins at scale. This platform supports earlier disease detection, prediction of outcomes, and more precise, personalized care across domains.

Rather than focusing on a single condition, the initiative is advancing high-impact applications across:

  • Cardiovascular health and heart failure. Digital twins integrate imaging, wearable data, and computational modeling to track hemodynamic changes over time—enabling earlier detection of decompensation and more informed intervention.
  • Neurological disease and cognitive decline. By combining imaging, gait, eye tracking, and behavioral data, digital twins improve the differentiation of disorders such as Alzheimer’s, Parkinson’s, and NPH while allowing longitudinal monitoring.
  • Sports and human performance. Digital twins support athlete monitoring through wearable data, motion analysis, and physiological modeling—advancing injury prevention, recovery, and performance optimization.

Across these domains, the approach is consistent: shifting from static snapshots to continuous, personalized models that evolve over time.

The Backbone: PRISM

At the core of this initiative is PRISM (Platform for Research Integration of Secure Multimodal Modeling), developed within CCDHI as a secure, FAIR, AI-enabled platform for digital twin research and deployment. PRISM integrates electronic health records, imaging, wearable data, motion and gait analysis, behavioral signals, and computational models into a unified environment for analysis and simulation.

By providing a reusable infrastructure, PRISM enables rapid development, validation, and deployment of digital twins across applications—eliminating the need for fragmented, disease-specific pipelines.

What Makes This Approach Different

  • Imaging-driven foundations. Advanced imaging provides quantitative insight into anatomy and physiology. Combined with segmentation and modeling, these data enable reconstruction of patient-specific structure and function.
  • Longitudinal wearable integration. Wearables capture continuous signals such as activity, heart rate, and sleep—allowing digital twins to evolve with the patient over time.
  • Behavioral and functional phenotyping. Eye tracking, gait analysis, and video-based assessment capture subtle changes in cognition and movement beyond traditional clinical metrics.
  • Physics-based modeling. Mechanistic models derived from imaging enable simulation of physiological processes and prediction of intervention outcomes.
  • Unified AI-enabled platform. PRISM integrates these data streams into a single environment, enabling multimodal analysis and discovery of composite biomarkers.

Why Duke Will Lead

Through CCDHI, Duke combines the capabilities required to lead digital twin development and deployment:

  • Integrated ecosystem: Seamless collaboration across engineering, medicine, nursing, and data science.
  • Imaging leadership: Advanced MRI, quantitative imaging, and analysis expertise.
  • Computational strength: Leadership in high performance computing, AI, and modeling.
  • Clinical scale: Access to diverse patient populations and longitudinal data.
  • Proven innovation: Established track record in digital twins, wearables, and translational research.

These strengths enable Duke not only to develop digital twins, but to deploy them in real-world settings.

Transformative Impact

The Duke Digital Twin Initiative establishes a unified framework that integrates imaging, physiology, behavior, and modeling into a single system. This enables identification of multimodal biomarkers, earlier detection of disease, and more precise, personalized care.

By building a reusable, scalable platform through CCDHI, Duke is accelerating digital twin development across domains—from cardiovascular disease and neuroscience to human performance. This initiative positions Duke as a national leader in digital twin research and deployment and creates a durable foundation for innovation across medicine and health.