RT Journal Article
SR Electronic
T1 Deep Learning in Neuroradiology
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1776
OP 1784
DO 10.3174/ajnr.A5543
VO 39
IS 10
A1 G. Zaharchuk
A1 E. Gong
A1 M. Wintermark
A1 D. Rubin
A1 C.P. Langlotz
YR 2018
UL http://www.ajnr.org/content/39/10/1776.abstract
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