Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy

Abstract

Purpose: To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy.

Methods: A deep-learning convolutional neural network (CNN) architecture, VGG16, was employed for this study. A transfer learning process was implemented to retrain the CNN for robust OCTA classification. One dataset, consisting of images of 32 healthy eyes, 75 eyes with diabetic retinopathy (DR), and 24 eyes with diabetes but no DR (NoDR), was used for training and cross-validation. A second dataset consisting of 20 NoDR and 26 DR eyes was used for external validation. To demonstrate the feasibility of using artificial intelligence (AI) screening of DR in clinical environments, the CNN was incorporated into a graphical user interface (GUI) platform.

Results: With the last nine layers retrained, the CNN architecture achieved the best performance for automated OCTA classification. The cross-validation accuracy of the retrained classifier for differentiating among healthy, NoDR, and DR eyes was 87.27%, with 83.76% sensitivity and 90.82% specificity. The AUC metrics for binary classification of healthy, NoDR, and DR eyes were 0.97, 0.98, and 0.97, respectively. The GUI platform enabled easy validation of the method for AI screening of DR in a clinical environment.

Conclusions: With a transfer learning process for retraining, a CNN can be used for robust OCTA classification of healthy, NoDR, and DR eyes. The AI-based OCTA classification platform may provide a practical solution to reducing the burden of experienced ophthalmologists with regard to mass screening of DR patients.

Translational relevance: Deep-learning-based OCTA classification can alleviate the need for manual graders and improve DR screening efficiency.

Keywords: artificial intelligence; deep learning; detection; diabetic retinopathy; screening.

Figure 1.

The deep learning CNN used for OCTA DR detection is VGG16, a network that contains 16 trainable layers: convolution (Conv) and fully connected (FC) layers. The corresponding output layer dimensions of each layer is shown below each block. All convolution and fully connected layers are followed by a ReLU activation function. The softmax layer is a fully connected layer that is followed by a softmax activation function. Maxpool and Flatten layers are operational layers with no tunable parameters.

Figure 2.

A transfer learning performance study was conducted to determine how many layers are necessary for effective transfer learning in OCTA images. Our model consisted of 16 retrainable layers. The additional graph in the right-hand corner shows that retraining nine layers satisfies the criteria of the one positive standard deviation rule.

Figure 3.

ROC curves for the cross-validation performance of the model for individual class performance (control, NoDR, and DR) and the average performance of the model.

Figure 4.

GUI platform for DR classification using OCTA.