Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy

TL;DR

This study demonstrates that transfer learning with a CNN can effectively classify diabetic retinopathy stages using OCTA images, enabling AI screening in clinical settings with high accuracy.

Contribution

The paper introduces a transfer learning approach with VGG16 CNN for automated OCTA-based diabetic retinopathy detection, suitable for clinical AI screening.

Findings

Achieved 87.27% accuracy in classifying healthy, NoDR, and NPDR eyes.

High AUC metrics of 0.97-0.98 for different classifications.

GUI platform facilitates clinical validation of the method.

Abstract

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.