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 the cervical spine, with smaller errors, less computation time and improved robustness when compared to the clinical standard. • In segmentation models, Deep Learning methods show promising results with the application of fully automatic convolutional neural network (CNN) models for CT and MR imaging. • In cervical spine analysis, the biomechanical features are most often studied using finite element models. The application of artificial neural networks and support vector machine models looks promising for other classification purposes. • Machine Learning is the future of This systematic review may function as a reference paper, providing an overview of all available methods reported in literature for authors conducting research on computer aided diagnostics of the cervical spine.