Literature DB >> 28438070

Creation of a novel simulator for minimally invasive neurosurgery: fusion of 3D printing and special effects.

Peter Weinstock1,2,3, Roberta Rehder4, Sanjay P Prabhu2,5,3, Peter W Forbes6, Christopher J Roussin1,2,3, Alan R Cohen4.   

Abstract

OBJECTIVE Recent advances in optics and miniaturization have enabled the development of a growing number of minimally invasive procedures, yet innovative training methods for the use of these techniques remain lacking. Conventional teaching models, including cadavers and physical trainers as well as virtual reality platforms, are often expensive and ineffective. Newly developed 3D printing technologies can recreate patient-specific anatomy, but the stiffness of the materials limits fidelity to real-life surgical situations. Hollywood special effects techniques can create ultrarealistic features, including lifelike tactile properties, to enhance accuracy and effectiveness of the surgical models. The authors created a highly realistic model of a pediatric patient with hydrocephalus via a unique combination of 3D printing and special effects techniques and validated the use of this model in training neurosurgery fellows and residents to perform endoscopic third ventriculostomy (ETV), an effective minimally invasive method increasingly used in treating hydrocephalus. METHODS A full-scale reproduction of the head of a 14-year-old adolescent patient with hydrocephalus, including external physical details and internal neuroanatomy, was developed via a unique collaboration of neurosurgeons, simulation engineers, and a group of special effects experts. The model contains "plug-and-play" replaceable components for repetitive practice. The appearance of the training model (face validity) and the reproducibility of the ETV training procedure (content validity) were assessed by neurosurgery fellows and residents of different experience levels based on a 14-item Likert-like questionnaire. The usefulness of the training model for evaluating the performance of the trainees at different levels of experience (construct validity) was measured by blinded observers using the Objective Structured Assessment of Technical Skills (OSATS) scale for the performance of ETV. RESULTS A combination of 3D printing technology and casting processes led to the creation of realistic surgical models that include high-fidelity reproductions of the anatomical features of hydrocephalus and allow for the performance of ETV for training purposes. The models reproduced the pulsations of the basilar artery, ventricles, and cerebrospinal fluid (CSF), thus simulating the experience of performing ETV on an actual patient. The results of the 14-item questionnaire showed limited variability among participants' scores, and the neurosurgery fellows and residents gave the models consistently high ratings for face and content validity. The mean score for the content validity questions (4.88) was higher than the mean score for face validity (4.69) (p = 0.03). On construct validity scores, the blinded observers rated performance of fellows significantly higher than that of residents, indicating that the model provided a means to distinguish between novice and expert surgical skills. CONCLUSIONS A plug-and-play lifelike ETV training model was developed through a combination of 3D printing and special effects techniques, providing both anatomical and haptic accuracy. Such simulators offer opportunities to accelerate the development of expertise with respect to new and novel procedures as well as iterate new surgical approaches and innovations, thus allowing novice neurosurgeons to gain valuable experience in surgical techniques without exposing patients to risk of harm.

Entities:  

Keywords:  3D printing technology; ACGME = Accreditation Council of Graduate Medical Education; CSF = cerebrospinal fluid; ETV = endoscopic third ventriculostomy; OSATS = Objective Structured Assessment of Technical Skills; PGY = postgraduate year; endoscopic third ventriculostomy; hydrocephalus; minimally invasive neurosurgery; residency; simulation; surgical trainers

Mesh:

Year:  2017        PMID: 28438070     DOI: 10.3171/2017.1.PEDS16568

Source DB:  PubMed          Journal:  J Neurosurg Pediatr        ISSN: 1933-0707            Impact factor:   2.375


  18 in total

1.  Evaluation methods and impact of simulation-based training in pediatric surgery: a systematic review.

Authors:  Shinichiro Yokoyama; Kenichi Mizunuma; Yo Kurashima; Yusuke Watanabe; Tomoko Mizota; Saseem Poudel; Takanori Kikuchi; Fujimi Kawai; Toshiaki Shichinohe; Satoshi Hirano
Journal:  Pediatr Surg Int       Date:  2019-08-08       Impact factor: 1.827

Review 2.  3D printing in spine surgery.

Authors:  Evan D Sheha; Sapan D Gandhi; Matthew W Colman
Journal:  Ann Transl Med       Date:  2019-09

3.  Stereoscopic three-dimensional visualization: interest for neuroanatomy teaching in medical school.

Authors:  Timothée Jacquesson; Emile Simon; Corentin Dauleac; Loïc Margueron; Philip Robinson; Patrick Mertens
Journal:  Surg Radiol Anat       Date:  2020-02-29       Impact factor: 1.246

4.  3D-Printed Disease Models for Neurosurgical Planning, Simulation, and Training.

Authors:  Chul-Kee Park
Journal:  J Korean Neurosurg Soc       Date:  2022-06-28

5.  Application of nondestructive mechanical characterization testing for creating in vitro vessel models with material properties similar to human neurovasculature.

Authors:  Nicholas G Norris; William C Merritt; Timothy A Becker
Journal:  J Biomed Mater Res A       Date:  2021-10-06       Impact factor: 4.854

Review 6.  Recent approaches in clinical applications of 3D printing in neonates and pediatrics.

Authors:  Sukanya V S; Nalinikanta Panigrahy; Subha Narayan Rath
Journal:  Eur J Pediatr       Date:  2020-10-06       Impact factor: 3.183

7.  Design and validation of a 3D-printed simulator for endoscopic third ventriculostomy.

Authors:  Junhao Zhu; Jin Yang; Chao Tang; Zixiang Cong; Xiangming Cai; Chiyuan Ma
Journal:  Childs Nerv Syst       Date:  2019-11-12       Impact factor: 1.475

8.  3D Brain Imaging in Vascular Segmentation of Cerebral Venous Sinuses.

Authors:  Asli Beril Karakas; Figen Govsa; Mehmet Asım Ozer; Cenk Eraslan
Journal:  J Digit Imaging       Date:  2019-04       Impact factor: 4.056

9.  Investigating accuracy of 3D printed liver models with computed tomography.

Authors:  Jan Witowski; Nicole Wake; Anna Grochowska; Zhonghua Sun; Andrzej Budzyński; Piotr Major; Tadeusz Jan Popiela; Michał Pędziwiatr
Journal:  Quant Imaging Med Surg       Date:  2019-01

10.  Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning.

Authors:  Eleanor C Mackle; Jonathan Shapey; Efthymios Maneas; Shakeel R Saeed; Robert Bradford; Sebastien Ourselin; Tom Vercauteren; Adrien E Desjardins
Journal:  J Vis Exp       Date:  2020-07-14       Impact factor: 1.355

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