A Real-Time Uncertainty-Aware Digital Twin Framework for Intra-Operative Valve Surgery

SUMMARY & CONCLUSIONSCardiac-valve repair is increasingly performed with minimal invasive approaches including robotic and catheter techniques that hinges on fragmented imaging modalities. Heart valve repair or replacement decisions are often made intraoperatively on the arrested heart, limiting the ability to assess the success of a repair prior to completing the surgery. It is difficult to monitor and predict intra-operative hemodynamic pressure on the arrested heart including device telemetry, which leaves the surgical team without a reliable, physics-based prediction of valve mechanics prior to the valve repair. Existing patient-specific simulations often run off-line; they omit closed-loop data assimilation, uncertainty policing, and regulatory-grade software controls. In this paper, we present a unified, intra-operative digital twin framework that closes the cyber-physical loop between the patient and surgical devices while conforming to ASME V&V-40, FDA modeling guidance, and IEC 62304 Class C software. The architecture integrates five elements: (i) a HL7/DICOM/IEEE 1588 data pipeline that harmonizes multimodal imaging, physiologic waveforms, robotic telemetry, and workflow video; (ii) a Bayesian assimilation engine that fuses these streams into a patient-specific latent state and covariance in real time; (iii) a GPU-accelerated, partitioned multi-physics core that couples reduced-order leaflet mechanics, immersed-boundary lattice Boltzmann hemodynamics, and lumped-parameter circulation via a predictor-corrector coupler; (iv) a generalized polynomial chaos ensemble that propagates both aleatory and epistemic uncertainty; and (v) a decision support platform that pairs an uncertainty-aware model predictive controller (MPC) with a reinforcement learning supervisor. They are embedded within a tiered human-in-the-loop authority arbiter. An implementation roadmap—(i) bench-top hardware-in-the-loop, (ii) pre-clinical translation, and (iii) regulatory submission—is outlined to guide deployment and validation.