Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration

Cecilia S. Lee; Doug M. Baughman; Aaron Y. Lee

doi:10.1101/094276

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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.

Methods Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR. OCT macula scans were obtained by Heidelberg Spectralis, and each OCT scan was linked to EMR clinical endpoints extracted from EPIC. The central 11 images were selected from each OCT scan of two cohorts of patients: normal and AMD. Cross-validation was performed using a random subset of patients. Receiver operator curves (ROC) were constructed at an independent image level, macular OCT level, and patient level.

Main outcome measure Area under the ROC.

Results Of a recent extraction of 2.6 million OCT images linked to clinical datapoints from the EMR, 52,690 normal macular OCT images and 48,312 AMD macular OCT images were selected. A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an area under the ROC of 92.78% with an accuracy of 87.63%. At the macula level, we achieved an area under the ROC of 93.83% with an accuracy of 88.98%. At a patient level, we achieved an area under the ROC of 97.45% with an accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69% respectively.

Conclusions Deep learning techniques achieve high accuracy and is effective as a new image classification technique. These findings have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future.

Footnotes

Financial support: National Eye Institute, Bethesda, Maryland (grant no.: K23EY02492 [C.S.L.]); Latham Vision Science Innovation Grant, Seattle WA (C.S.L., D.M.B.) and Research to Prevent Blindness, Inc., New York, New York (C.S.L., A.Y.L.). The sponsors and funding organizations had no role in the design or conduct of this research.
Conflict of interest: No conflicting relationship exists for any author.
Abbreviations and acronyms
AMD
Age-related Macular Degeneration
AUROC
Area Under the Receiver Operator Curve
CAD
Computer Aided Diagnosis
EMR
Electronic Medical Records
FDA
Food and Drug Administration
GPU
Graphics Processing Unit
ICD-9
International Classification of Diseases, 9th edition
OCT
Optical Coherence Tomography
ReLU
Rectified Linear Unit
ROC
Receiver Operator Curve
RPE
Retinal Pigmented Epithelium
SD
Standard Deviation
UW
University of Washington

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