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.
ABBREVIATIONS:
- AD
- Alzheimer disease
- ADNI
- Alzheimer's Disease Neuroimaging Initiative
- ASL
- arterial spin-labeling
- CNN
- convolutional neural network
- MCI
- mild cognitive impairment
- NC
- normal control
Footnotes
Drs Rubin and Langlotz are co-senior authors.
Disclosures: Greg Zaharchuk—UNRELATED: Grants/Grants Pending: GE Healthcare, National Institutes of Health*; OTHER RELATIONSHIPS: Subtle Medical Inc, cofounder and equity relationship. Enhao Gong—UNRELATED: Board Membership: GE Healthcare, Comments: research grant*; Patents (Planned, Pending or Issued): Subtle Medical Inc; Stock/Stock Options: Subtle Medical Inc. Max Wintermark—UNRELATED: Board Membership: GE-NFL Advisory Board. Daniel Rubin—UNRELATED: Grants/Grants Pending: National Institutes of Health*. Curtis P. Langlotz—OTHER RELATIONSHIPS: Montage Healthcare Solutions, Comments: founder, shareholder, board member; received consulting fees and travel reimbursement. *Money paid to the institution.
- © 2018 by American Journal of Neuroradiology
Indicates open access to non-subscribers at www.ajnr.org