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 using fi...

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Bibliographic Details
Published in:	Brain & spine Vol. 2; p. 101666
Main Authors:	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.
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01-01-2022 Elsevier
Subjects:	Cervical spine Computer aided diagnostics Image analysis Machine learning Radiological imaging Review Computer aided diagnostics Image analysis Radiological imaging Cervical spine Machine learning
Online Access:	Get full text
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Summary:	•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.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 Co-first author.
ISSN:	2772-5294 2772-5294
DOI:	10.1016/j.bas.2022.101666