• Matteo de Notaris, Thomaz Topczewski, Michelangelo de Angelis, Joaquim Enseñat, Isam Alobid, Amer Mustafa Gondolbleu, Guadalupe Soria, Joan Berenguer Gonzalez, Enrique Ferrer, and Alberto Prats-Galino.
• Department of Neurosurgery, Hospital Clinic, Faculty of Medicine, Universitat de Barcelona, Barcelona, Spain. matteodenotaris@gmail.com
• World Neurosurg. 2013 Feb 1;79(2 Suppl):S16.e9-13.

ObjectiveThe goal of the present article was to describe our dissection training system applied to a variety of endoscopic endonasal approaches. It allows one to perform a 3D virtual dissection of the desired approach and to analyze and quantify critical surgical measurements.MethodsAll the human cadaveric heads were dissected at the Laboratory of Surgical Neuro-Anatomy (LSNA) of the University of Barcelona (Spain). The model surgical training protocol was designed as follows: 1) virtual dissection of the selected approach using our dissection training 3D model; 2) preliminary exploration of each specimen using a second 3D model based on a preoperative computed tomographic scan; 3) cadaveric anatomic dissection with the aid of a neuronavigation system; and 4) quantification and analysis of the collected data.ResultsThe virtual dissection of the selected approach, preliminary exploration of each specimen, a real laboratory dissection experience, and finally, the analysis of data retrieved during the dissection step was a complete method for training manual dexterity and hand-eye coordination and to improve the general knowledge of surgical approaches.ConclusionsThe present model results are found to be effective, providing a valuable representation of the surgical anatomy as well as a 3D visual feedback, thus improving study, design, and execution in a variety of approaches. Such a system can also be developed as a preoperative planning tool that will allow the neurosurgeon to practice and manipulate 3D representations of the critical anatomic landmarks involved in the endoscopic endonasal approaches to the skull base.Copyright © 2013 Elsevier Inc. All rights reserved.

28 keywords...

hide…