Pediatric Spine Segmentation and Modeling Using Machine Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU134032" target="_blank" >RIV/00216305:26220/19:PU134032 - isvavai.cz</a>

Alternative codes found

RIV/65269705:_____/19:00072859

Result on the web

<a href="https://ieeexplore.ieee.org/abstract/document/8970894" target="_blank" >https://ieeexplore.ieee.org/abstract/document/8970894</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT48472.2019.8970894" target="_blank" >10.1109/ICUMT48472.2019.8970894</a>

Result language

angličtina

Original language name

Pediatric Spine Segmentation and Modeling Using Machine Learning

Original language description

Scoliosis embodies the most frequent threedimensional spinal deformity in children. Only timely treatment during the growth of the spine may significantly reduce related health problems inflicted by the deformity on adults. The results obtained via conservative therapy are problematic, and a certain degree of curvature already requires surgical treatment that at the time of writing consists of repeated spinal surgeries posing a high risk of complications. The aim is to use a spine model for computer based simulation of changes in the stress on the spine during idiopathic and syndromic deformity correction via vertebral osteotomy. A machine-learning toolbox for 3D Slicer has been developed. The toolbox has a form of an application extension. Preprocessing of the data, training and usage of the classifier is possible through a simple and modern graphical user interface. The extension is capable of performing a variety of helpful tasks such as an analysis of the impact of the size of the training vector and feature selection on classifier precision. The results suggest that the training vector size can be minimized for all of the tested classifiers. Furthermore, the random forest classifier's performance seems to be resistant to training parameter changes. Support vector machine is sensitive to training parameter changes with optimal values concentrated in a narrow feature space.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20601 - Medical engineering

Project

<a href="/en/project/NV18-08-00459" target="_blank" >NV18-08-00459: Spatial Analysis of the Force Load on a Deformed Developing Spine, and Corrective Force Modelling Applied to Minimize the Scope of a Scoliosis Surgery.</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

978-1-7281-5763-4

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

1-5

Publisher name

Neuveden

Place of publication

neuveden

Event location

Dublin

Event date

Oct 28, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000540651700045