BACKGROUND: Brain tumor surgeries are associated with a high technical and personal effort. The required interactions between the surgeon and the technical components, such as neuronavigation, surgical instruments and intraoperative imaging, are complex and demand innovative training solutions and standardized evaluation methods. Phantom-based training systems could be useful in complementing the existing surgical education and training. METHODS: A prototype of a phantom-based training system was developed, intended for standardized training of important aspects of brain tumor surgery based on real patient data. The head phantom consists of a three-part construction that includes a reusable base and adapter, as well as a changeable module for single use. Training covers surgical planning of the optimal access path, the setup of the navigation system including the registration of the head phantom, as well as the navigated craniotomy with real instruments. Tracked instruments during the simulation and predefined access paths constitute the basis for the essential objective training feedback. RESULTS: The prototype was evaluated in a pilot study by assistant physicians at different education levels. They performed a complete simulation and a final assessment using an evaluation questionnaire. The analysis of the questionnaire showed the evaluation result as "good" for the phantom construction and the used materials. The learning effect concerning the navigated planning was evaluated as "very good", as well as having the effect of increasing safety for the surgeon before planning and conducting craniotomies independently on patients. CONCLUSIONS: The training system represents a promising approach for the future training of neurosurgeons. It aims to improve surgical skill training by creating a more realistic simulation in a non-risk environment. Hence, it could help to bridge the gap between theoretical and practical training with the potential to benefit both physicians and patients.
BACKGROUND:Brain tumor surgeries are associated with a high technical and personal effort. The required interactions between the surgeon and the technical components, such as neuronavigation, surgical instruments and intraoperative imaging, are complex and demand innovative training solutions and standardized evaluation methods. Phantom-based training systems could be useful in complementing the existing surgical education and training. METHODS: A prototype of a phantom-based training system was developed, intended for standardized training of important aspects of brain tumor surgery based on real patient data. The head phantom consists of a three-part construction that includes a reusable base and adapter, as well as a changeable module for single use. Training covers surgical planning of the optimal access path, the setup of the navigation system including the registration of the head phantom, as well as the navigated craniotomy with real instruments. Tracked instruments during the simulation and predefined access paths constitute the basis for the essential objective training feedback. RESULTS: The prototype was evaluated in a pilot study by assistant physicians at different education levels. They performed a complete simulation and a final assessment using an evaluation questionnaire. The analysis of the questionnaire showed the evaluation result as "good" for the phantom construction and the used materials. The learning effect concerning the navigated planning was evaluated as "very good", as well as having the effect of increasing safety for the surgeon before planning and conducting craniotomies independently on patients. CONCLUSIONS: The training system represents a promising approach for the future training of neurosurgeons. It aims to improve surgical skill training by creating a more realistic simulation in a non-risk environment. Hence, it could help to bridge the gap between theoretical and practical training with the potential to benefit both physicians and patients.
Authors: Felipe Wilker Grillo; Victor Hugo Souza; Renan Hiroshi Matsuda; Carlo Rondinoni; Theo Zeferino Pavan; Oswaldo Baffa; Helio Rubens Machado; Antonio Adilton Oliveira Carneiro Journal: 3D Print Med Date: 2018-03-13