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

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.

FIGURE 1:

Schematic of the deep learning model used. A total of 21 layers with Rectified Linear Unit (ReLU) activations were used.

FIGURE 2:

Learning curve of the training of the neural network with accuracy (A) and loss (B). Each iteration represents 100 images being trained on the neural network.

FIGURE 3:

Receiver-operator curves of three levels of classification. Image level classification was performed by considering each image independently. Macula level classification was performed by averaging the probabilities of all the images in a single macular volume. Patient level classification was performed by averaging the probabilities of all the images belonging to a single patient.

FIGURE 4:

Examples of identification of pathology by deep learning algorithm. Images of optical coherence tomography (OCT) with age-related macular degeneration (AMD) pathology (A, B, C) are used as input images and hotspots (D, E, F) are identified using an occlusion test from the deep learning algorithm. The intensity of the color is determined by the drop in the probability of being labeled AMD when occluded.