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Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods
• Neural network approaches show the most potential for automated image analysis of thecervical spine. • Fully automatic convolutional neural network (CNN) models are promising Deep Learning methods for segmentation. • In cervical spine analysis, the biomechanical features are most often studied usi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729832/ https://www.ncbi.nlm.nih.gov/pubmed/36506292 http://dx.doi.org/10.1016/j.bas.2022.101666 |
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author | Goedmakers, C.M.W. Pereboom, L.M. Schoones, J.W. de Leeuw den Bouter, M.L. Remis, R.F. Staring, M. Vleggeert-Lankamp, C.L.A. |
author_facet | Goedmakers, C.M.W. Pereboom, L.M. Schoones, J.W. de Leeuw den Bouter, M.L. Remis, R.F. Staring, M. Vleggeert-Lankamp, C.L.A. |
author_sort | Goedmakers, C.M.W. |
collection | PubMed |
description | • Neural network approaches show the most potential for automated image analysis of thecervical spine. • Fully automatic convolutional neural network (CNN) models are promising Deep Learning methods for segmentation. • In cervical spine analysis, the biomechanical features are most often studied using finiteelement models. • The application of artificial neural networks and support vector machine models looks promising for classification purposes. • This article provides an overview of the methods for research on computer aided imaging diagnostics of the cervical spine. |
format | Online Article Text |
id | pubmed-9729832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-97298322022-12-09 Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods Goedmakers, C.M.W. Pereboom, L.M. Schoones, J.W. de Leeuw den Bouter, M.L. Remis, R.F. Staring, M. Vleggeert-Lankamp, C.L.A. Brain Spine Review • Neural network approaches show the most potential for automated image analysis of thecervical spine. • Fully automatic convolutional neural network (CNN) models are promising Deep Learning methods for segmentation. • In cervical spine analysis, the biomechanical features are most often studied using finiteelement models. • The application of artificial neural networks and support vector machine models looks promising for classification purposes. • This article provides an overview of the methods for research on computer aided imaging diagnostics of the cervical spine. Elsevier 2022-11-14 /pmc/articles/PMC9729832/ /pubmed/36506292 http://dx.doi.org/10.1016/j.bas.2022.101666 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Review Goedmakers, C.M.W. Pereboom, L.M. Schoones, J.W. de Leeuw den Bouter, M.L. Remis, R.F. Staring, M. Vleggeert-Lankamp, C.L.A. Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods |
title | Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods |
title_full | Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods |
title_fullStr | Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods |
title_full_unstemmed | Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods |
title_short | Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods |
title_sort | machine learning for image analysis in the cervical spine: systematic review of the available models and methods |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729832/ https://www.ncbi.nlm.nih.gov/pubmed/36506292 http://dx.doi.org/10.1016/j.bas.2022.101666 |
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