Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images
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Abstract
Objective
The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD).
Design
EMR and OCT database study. SUBJECTS: Normal and AMD patients who had a macular OCT.
Citation impact
651
total citations
- FWCI
- 46.05
- Percentile
- 100%
- References
- 27
Citations per year
Authors
3Topics & keywords
Topics
Keywords
- Optical coherence tomography
- Medicine
- Macular degeneration
- Receiver operating characteristic
- Artificial intelligence
- Deep learning
- Ophthalmology
- Computer science
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