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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...

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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
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.
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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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