Literature DB >> 29927431

[A classification of implants for reconstruction of the anterior and middle supporting columns of the spine].

S N Nekhlopochin1, A S Nekhlopochin1, A I Shvets1.   

Abstract

OBJECTIVE: The study objective was to classify implants for anterior interbody fusion, depending on their design features and functional capabilities, to optimize the choice of endoprosthesis designs for reconstructive surgery of the spin.
MATERIAL AND METHODS: We analyzed information provided in advertising brochures, annotations, and communications describing the designs of vertebral body endoprostheses. To study the design features of various implants, we selected 25 implant types with the structural designs of major units typical of vertebral body replacement systems. We performed static tests of mechanical features of anterior interbody fusion systems using special equipment and mathematical modeling based on the finite element technique to determine features of the stressed-deformed state in replacement of the vertebral bodies with artificial implants of various designs. RESULTS AND DISCUSSION: The analysis results define the prerogative of combined telescopic designs with axisymmetric accommodation of compression stresses, which enable reconstruction and stabilization of the spinal motion segment without additional fixation by a ventral plate. The classification of endoprostheses enables evaluation of advantages and disadvantages of various implants to objectively assess the mechanisms of potential postoperative complications and to prevent them. The presented data may facilitate the optimal choice of an implant with allowance for the peculiarities of a clinical situation in each particular case.

Keywords:  classification of implants for anterior fusion; spinal diseases and injuries; surgical treatment

Mesh:

Year:  2018        PMID: 29927431     DOI: 10.17116/neiro201882397

Source DB:  PubMed          Journal:  Zh Vopr Neirokhir Im N N Burdenko        ISSN: 0042-8817


  1 in total

1.  Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network.

Authors:  Zijiang Zhu; Hang Chen; Song Xie; Yi Hu; Jing Chang
Journal:  Comput Intell Neurosci       Date:  2022-07-21
  1 in total

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