PT  - JOURNAL ARTICLE
AU  - G. Zaharchuk
AU  - E. Gong
AU  - M. Wintermark
AU  - D. Rubin
AU  - C.P. Langlotz
TI  - Deep Learning in Neuroradiology
AID  - 10.3174/ajnr.A5543
DP  - 2018 Feb 01
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2018/02/01/ajnr.A5543.short
4100  - http://www.ajnr.org/content/early/2018/02/01/ajnr.A5543.full
AB  - 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.ADAlzheimer diseaseADNIAlzheimer's Disease Neuroimaging InitiativeASLarterial spin-labelingCNNconvolutional neural networkMCImild cognitive impairmentNCnormal control