  Purpose: Digital twins are virtual interactive models of the real world,
exhibiting identical behavior and properties. In surgical applications,
computational analysis from digital twins can be used, for example, to enhance
situational awareness. Methods: We present a digital twin framework for
skull-base surgeries, named Twin-S, which can be integrated within various
image-guided interventions seamlessly. Twin-S combines high-precision optical
tracking and real-time simulation. We rely on rigorous calibration routines to
ensure that the digital twin representation precisely mimics all real-world
processes. Twin-S models and tracks the critical components of skull-base
surgery, including the surgical tool, patient anatomy, and surgical camera.
Significantly, Twin-S updates and reflects real-world drilling of the
anatomical model in frame rate. Results: We extensively evaluate the accuracy
of Twin-S, which achieves an average 1.39 mm error during the drilling process.
We further illustrate how segmentation masks derived from the continuously
updated digital twin can augment the surgical microscope view in a mixed
reality setting, where bone requiring ablation is highlighted to provide
surgeons additional situational awareness. Conclusion: We present Twin-S, a
digital twin environment for skull-base surgery. Twin-S tracks and updates the
virtual model in real-time given measurements from modern tracking
technologies. Future research on complementing optical tracking with
higher-precision vision-based approaches may further increase the accuracy of
Twin-S.
