A Comprehensive Survey on Surgical Digital Twin

TL;DR

This survey synthesizes the burgeoning field of Surgical Digital Twins (SDTs), articulating a taxonomy by purpose, fidelity, and data sources, and surveying advances in deformable registration, real-time co-simulation, AR/VR guidance, and edge-cloud orchestration. It contrasts non-robotic and robot-in-the-loop SDTs, and clarifies validation, safety, and governance needs that must be addressed for clinical translation. The authors argue for trustworthy, standards-aligned SDTs, supported by rigorous validation, interoperable data models, and privacy-preserving data sharing, to deliver measurable improvements in intraoperative decision-making and patient outcomes. By mapping the modeling-to-execution-to-systems chain and outlining open problems, the paper provides a roadmap for translating SDTs from laboratory prototypes to routinely deployed clinical tools.

Abstract

With the accelerating availability of multimodal surgical data and real-time computation, Surgical Digital Twins (SDTs) have emerged as virtual counterparts that mirror, predict, and inform decisions across pre-, intra-, and postoperative care. Despite promising demonstrations, SDTs face persistent challenges: fusing heterogeneous imaging, kinematics, and physiology under strict latency budgets; balancing model fidelity with computational efficiency; ensuring robustness, interpretability, and calibrated uncertainty; and achieving interoperability, privacy, and regulatory compliance in clinical environments. This survey offers a critical, structured review of SDTs. We clarify terminology and scope, propose a taxonomy by purpose, model fidelity, and data sources, and synthesize state-of-the-art achievements in deformable registration and tracking, real-time simulation and co-simulation, AR/VR guidance, edge-cloud orchestration, and AI for scene understanding and prediction. We contrast non-robotic twins with robot-in-the-loop architectures for shared control and autonomy, and identify open problems in validation and benchmarking, safety assurance and human factors, lifecycle "digital thread" integration, and scalable data governance. We conclude with a research agenda toward trustworthy, standards-aligned SDTs that deliver measurable clinical benefit. By unifying vocabulary, organizing capabilities, and highlighting gaps, this work aims to guide SDT design and deployment and catalyze translation from laboratory prototypes to routine surgical care.

Figures (6)

• Figure 1: Digital Twin Ecosystem.
• Figure 2: Five-dimensional DT architecture. mihai2022digital.
• Figure 3: An illustration of agent-environment interaction in RL under the context of surgical robots qian2023deep.
• Figure 4: Components of the da Vinci Surgical System: patient-side cart, vision cart, surgeon console avgousti2020robotic.
• Figure 5: Networking architecture of the Human Digital Twinchen2024networking.