ObjectiveMicrovascular anastomosis is challenging, and training surgeons to develop and maintain skills is imperative. Current training models either miss the simulation of the surgical workflow, lack 3D key-hole space, need ethical approval, require special preparation, or lack realism. To circumvent these issues, this study describes the use of a mixed reality 3D printed model with integrated blood flow for training cerebral anastomosis and assesses its validity. MethodsDifferent-sized 3D-printed artificial micro artery models in a 3D brain space with a keyhole opening were used. The model was connected to a 4D simulator to induce pulsatile blood flow. Neurosurgical microscopes and exoscopes were used for visualization. Nine participants (n = 6 board-certified cerebrovascular neurosurgeons; n = 3 in-training) participated in the study and practiced anastomosis techniques with the simulator. Two senior, experienced vascular neurosurgeons mentored live teaching activity on the simulator. Participants completed a survey to score the face and content validity of the simulation on a 5-point Likert scale. Simulation time and anastomosis score differences between in-training and board-certified participants were compared for construct validity. ResultsParticipants scored the simulation difficulty similar to actual surgery, proving face validity. All participants agreed that practice on the provided simulator models would improve bypass techniques (μ = 4.67/5 ± 0.47) and instrument handling (μ = 4.56/5 ± 0.68). Board-certified participants had better anastomosis scores than in-training participants (non-significant difference). ConclusionsThe 4D simulator and the high-fidelity artificial 3D printed model effectively simulated actual bypass surgery in a key-hole fashion with blood flow abilities. Limited resources and preparation time are needed for the training setup. The model provides benefits in learning and maintaining anastomosis skills and allows for easy adaptation to different microanatomical scenarios.
Read full abstract