No abstract

Unilateral cervical facet joint dislocation (UCFJD) is the most frequently missed cervical spine injury on plain radiographs. If left untreated, UCFJD can progress to bilateral cervical facet joint dislocation. Given the complexity of cervical facet joint dislocations, radiologists rely on metaphorical signs to identify them on radiographs. The "Bowtie" and "laminar space" signs represent UCFJD on plain radiographs. The "reversed hamburger", "naked facet" and "headphones" signs represent cervical facet joint dislocations on axial cross-sectional imaging. Illustrating these signs in an engaging manner facilitates pattern-based recognition, which can benefit trainees and radiologists. Moreover, pattern-based recognition can be applied to machine learning.

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: Intramedullary lesions can be challenging to diagnose given the wide range of possible pathologies. Each lesion has unique clinical and imaging features, which are best evaluated on magnetic resonance imaging. Radiological imaging is unique with rich, descriptive patterns and classic signs—which are often metaphorical. In this review, we present a collection of classic MRI signs, ranging from neoplastic to non-neoplastic lesions, within the spinal cord. The differential diagnosis (DD) of intramedullary lesions can be narrowed down by careful analysis of the classic signs and pattern of involvement in the spinal cord. Furthermore, the signs are illustrated memorably with emphasis on the pathophysiology, mimics and pitfalls. Artificial intelligence (AI) algorithms, particularly deep learning, have made remarkable progress in image recognition tasks. The classic signs and related illustrations can enhance a pattern recognition approach in diagnostic radiology. Deep learning can potentially be designed to distinguish neoplastic from non-neoplastic processes by pattern recognition of the classic MRI signs.