Literature DB >> 31320548

Development and evaluation of a craniocerebral model with tactile-realistic feature and intracranial pressure for neurosurgical training.

Zongchao Yi1,2, Bingwei He1,2, Yuqing Liu2,3, Shenyue Huang2,3, Wenyao Hong2,3.   

Abstract

OBJECTIVE: In this article, a craniocerebral model is introduced for neurosurgical training, which is patient-specific, tactile-realistic, and with adjustable intracranial pressure.
METHODS: The patient-specific feature is achieved by modeling from CT scans and magnetic resonance images (MRI). The brain tissue model is built by the hydrogel casting technique, while scalp, skull, vasculature, and lateral ventricles are all-in-one fabricated by three-dimensional (3D) printing. A closed-loop system is integrated to monitor and control the intracranial pressure. 3D measurements, mechanical tests, and simulated external ventricular drain (EVD) placement procedures are conducted on the model.
RESULTS: A neurosurgical training model is completed with high accuracy (mean deviation 0.36 mm). The hydrogel brain tissue has a stiffness more similar to that of a real brain than the common 3D printed materials. The elasticity modulus of hydrogel brain tissue model is E=25.71 kPa, compared with our softest 3D printed material with E=1.14×103 kPa. Ten experienced surgeons rate the tactile realness of the neurosurgical training model at an average point of 4.25 on a scale from 1 (strongly negative) to 5 (strongly positive). The neurosurgical training model is also rated to be realistic in size (4.82), anatomy (4.70), and effective as an aid to improve blind EVD placement skills (4.65).
CONCLUSIONS: The neurosurgical training model can provide trainee surgeons with realistic experience in both tactile feedbacks and craniocerebral anatomy, improving their surgical skills. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  catheter; hydrocephalus; intracranial pressure; technology; ventricle

Mesh:

Year:  2019        PMID: 31320548     DOI: 10.1136/neurintsurg-2019-015008

Source DB:  PubMed          Journal:  J Neurointerv Surg        ISSN: 1759-8478            Impact factor:   5.836


  3 in total

1.  3D-Printed Disease Models for Neurosurgical Planning, Simulation, and Training.

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

Review 2.  3D printing in neurosurgery education: a review.

Authors:  Grace M Thiong'o; Mark Bernstein; James M Drake
Journal:  3D Print Med       Date:  2021-03-23

3.  Digital technology for orthognathic surgery training promotion: a randomized comparative study.

Authors:  Zhan Su; Yao Liu; Wenli Zhao; Yuanyan Bai; Nan Jiang; Songsong Zhu
Journal:  PeerJ       Date:  2022-08-02       Impact factor: 3.061

  3 in total

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