Deep Learning in Neuroradiology

doi:10.3174/ajnr.A5543

Paper

Deep Learning in Neuroradiology

Published Feb 1, 2018 · G. Zaharchuk, E. Gong, M. Wintermark

American Journal of Neuroradiology

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213

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2

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Abstract

SUMMARY: Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.

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Study Snapshot

Key takeawayDeep learning shows tremendous promise for neuroradiology, with potential applications in multimodal imaging and its use likely to rapidly grow in the future.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Deep Learning in Neuroradiology

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