Twin-S: a digital twin for skull base surgery

Area of Science:

• Neurosurgery
• Medical Technology
• Surgical Simulation

Background:

• Digital twins offer potential for enhancing surgical procedures by improving situational awareness.
• Skull base surgery presents unique challenges requiring precise visualization and real-time feedback.

Purpose of the Study:

• To introduce Twin-S, a novel digital twin framework specifically designed for skull base surgeries.
• To evaluate the accuracy and utility of Twin-S in a surgical context.

Main Methods:

• Twin-S integrates high-precision optical tracking and real-time simulation for image-guided interventions.
• Calibration routines ensure accurate reflection of real-world processes in the virtual model.
• The framework models and tracks surgical tools, patient anatomy, and cameras, updating the virtual model at 28 frames per second during drilling.

Main Results:

• Twin-S demonstrated high accuracy during drilling, with an average error of 1.39 mm.
• The framework provides augmented surgical views derived from the continuously updated virtual model.
• This leads to enhanced situational awareness for the surgeon.

Conclusions:

• Twin-S is a viable digital twin environment for skull base surgery, capturing and updating surgical progress in real time.
• Modern tracking technologies are key to Twin-S's real-time virtual model updates.
• Future integration of vision-based techniques may further improve Twin-S's accuracy.