A Comprehensive Survey on Surgical Digital Twin

1 A Comprehensive Survey on Surgical Digital Twin Afsah Sharaf Khan, Falong Fan, Doohwan DH Kim, Abdurrahman Alshareef, Dong Chen, Justin Kim, Ernest Carter, Bo Liu Senior Member, IEEE, Jerzy W. Rozenblit Senior Member, IEEE, Bernard Zeigler Fellow, IEEE 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 chal- lenges: fusing heterogeneous imaging, kinematics, and physiology under strict latency budgets; balancing model fidelity with com- putational 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 terminol- ogy 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. Index Terms—Digital twin; surgery; robotics; simulation; real- time systems; multimodal fusion; AR/VR guidance; I. INTRODUCTION A. Background and Significance a) Importance of Digital Twins in Healthcare: Digital Twin (DT) technology represents a significant evolution in the modeling and monitoring of physical entities, systems, and processes through their high-fidelity virtual counterparts. Un- like traditional static models, DTs offer dynamic, data-driven representations that are continuously updated with real-time information. In the healthcare sector, this paradigm shift has redefined the approach to patient care by facilitating real-time modeling of physiological conditions, disease progression, and therapeutic interventions [1]–[3]. In parallel, perioperative care has been framed through the lens of a continuously updated “human digital twin,” emphasizing multimodal data fusion and real-time decision support across the pre-, intra-, and postoperative continuum [4]. By integrating diverse data sources—including genomic profiles, advanced medical imaging, and IoT sensor out- puts—into comprehensive computational frameworks, DTs enable clinicians to move beyond retrospective analysis. These systems support predictive diagnostics, personalized treatment planning, and continuous health monitoring, effectively ush- ering in a new era of precision medicine [2], [3], [5]. Exam- ples of their application are already emerging in specialized domains. In cardiology, DTs simulate electrophysiological behaviors to anticipate arrhythmia risks and fine-tune pace- maker settings, enhancing patient safety and treatment efficacy [6], [7]. In oncology, they model tumor growth patterns and responses to various therapeutic regimens, thereby inform- ing minimally invasive ablation strategies and personalized treatment pathways [5], [8], [9]. Beyond these, vascular DTs combine mechanics-aware device/patient twins with learning- based risk indices to support endovascular planning Albertini et al. (2024), while probabilistic, risk-aware twins personalize radiotherapy fractionation in high-grade glioma [10]. The convergence of artificial intelligence, real-time analyt- ics, and sophisticated simulation techniques has enabled DTs to serve as foundational tools in modern healthcare. These technologies promise not only to enhance clinical outcomes but also to reduce healthcare costs and expand access to specialized medical expertise [1], [2], [11]. The continuous evolution of DT technology underscores its central role in transforming healthcare delivery, making it an indispensable component of the emerging precision medicine landscape. b) Emergence of Surgical Digital Twins: Within the broad spectrum of healthcare applications, surgical practice stands out as a particularly fertile ground for the deployment of Digital Twin technology. Surgical Digital Twins (SDTs) tran- scend the limitations of static anatomical models by provid- ing dynamic, patient-specific representations that encapsulate surgical procedures, operative environments, and physiologi- cal responses in real time [11]–[14]. Notably, mixed-reality anatomic twi

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