  Purpose: To test the feasibility of using deep learning for optical coherence
tomography angiography (OCTA) detection of diabetic retinopathy (DR). Methods:
A deep learning convolutional neural network (CNN) architecture VGG16 was
employed for this study. A transfer learning process was implemented to
re-train the CNN for robust OCTA classification. In order to demonstrate the
feasibility of using this method for artificial intelligence (AI) screening of
DR in clinical environments, the re-trained CNN was incorporated into a custom
developed GUI platform which can be readily operated by ophthalmic personnel.
Results: With last nine layers re-trained, CNN architecture achieved the best
performance for automated OCTA classification. The overall accuracy of the
re-trained classifier for differentiating healthy, NoDR, and NPDR was 87.27%,
with 83.76% sensitivity and 90.82% specificity. The AUC metrics for binary
classification of healthy, NoDR and DR 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. Conclusion: With a transfer leaning process to adopt
the early layers for simple feature analysis and to re-train the upper layers
for fine feature analysis, the CNN architecture VGG16 can be used for robust
OCTA classification of healthy, NoDR, and NPDR eyes. Translational Relevance:
OCTA can capture microvascular changes in early DR. A transfer learning process
enables robust implementation of convolutional neural network (CNN) for
automated OCTA classification of DR.
