Digital Twin for Surgical Training and Tailored-made Surgery (DT4Surgery)

PIs:

• Jerzy Rozenblit, Model and Base Design Laboratory, UArizona

• Mamadou Kaba Traore, University of Bordeaux

A digital twin (DT) is a model of a real system, which automatically learns and continually updates to represent the dynamics of the system in near real-time, using sensor data that reflect various aspects of its operating conditions, human experts with relevant domain knowledge, and its environment. However, this recent concept does not yet benefit from support tools for its effective implementation. This project aims at developing a software suite for modeling and simulation of a patient's digital twin, based on the DEVS (Discrete Event System Specification) paradigm, to be integrated into the Computer-Aided Surgical Trainer (CAST) prototype under development at the University of Arizona. This will allow DT-guided surgical training, thus paving the way for tailor-made surgical training by acting on the custom DT of each learning scenario instead of the electromechanical infrastructure that averagely represents a generic set of exercises.