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 2022-01, Vol.2, p.101666-101666, Article 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: Article
Language:English
Subjects:
Cervical spine
Computer aided diagnostics
Image analysis
Machine learning
Radiological imaging
Review
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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.
ISSN:2772-5294
2772-5294
DOI:10.1016/j.bas.2022.101666