Digital Twin Architecture for Therapy and Imaging

Abstract

Digital twins—computational representations of devices, environments, and patient physiology—offer transformative potential for medical device development by enabling simulation-based evaluation of scenarios that are difficult, dangerous, or impossible to reproduce physically. However, deployment in regulated environments demands rigorous credibility frameworks addressing verification, validation, and uncertainty quantification. This article proposes a comprehensive digital twin architecture tailored to regulated medical devices spanning therapy delivery and imaging workflows, emphasizing credibility as the foundation for trustworthy simulation evidence. The architecture decomposes platforms into core components, including device models capturing software logic and actuation behaviors, physics and environment models representing electromagnetic coupling and imaging physics, physiology models encompassing anatomy and tissue response dynamics, and data assimilation modules enabling patient-specific parameter tuning. Central infrastructure elements, including twin orchestrators, registries, and evidence layers, provide coordination, version control, and comprehensive documentation supporting reproducibility and regulatory compliance. The credibility framework establishes systematic protocols for code verification, solver verification, numerical stability assessment, and stratified validation across device settings, patient anatomies, and workflow variants, with explicit uncertainty quantification and traceability from risk controls to simulation evidence aligned with regulatory standards. Risk-informed applications enable hazard-to-scenario mapping, boundary condition exploration, fault injection testing, and combined fault mode analysis that expand verification coverage beyond traditional testing limitations. Controlled personalization frameworks define permissible parameter spaces and safety envelopes, treat patient-specific parameter estimation as verified algorithms rather than unbounded learning processes, implement privacy preservation through data minimization and secure governance, and establish guardrails preventing unsafe adaptations. Deployment considerations address computational infrastructure requirements, reproducibility protocols through containerization and numerical tolerance management, tool qualification processes for commercial and custom simulation codes and lifecycle management, ensuring version control, change management, and evidence archival throughout product lifecycles. The article concludes by examining broader development implications, including prototype reduction and shorter development cycles, alongside future directions encompassing standardized credibility reporting, shared physiological model libraries, and hybrid physics-data approaches integrating machine learning within physics-based constraints to maintain explainability and bounded behavior essential for regulatory acceptance.