Abstract
Purpose :
In the treatment planning of diabetic retinopathy (DR), fluorescein angiography (FA) has been essential for depicting the status of retinal vessels and pathological leakage, although there have been cases of allergic shock due to contrast dye. Recently, optical coherence tomography-angiography (OCTA) has gained popularity since it can delineate retinal vessels without contrast dye and the worry of allergic complications. However, a major drawback of OCTA is that it cannot depict vessel leakage, which is critical for treating DR, including diabetic macular edema. This study evaluated the ability of a novel artificial intelligence (AI)-assisted OCTA system also able to delineate the pathological leakage of retinal vessels.
Methods :
High-resolution wide-field (23 mm x 20 mm) OCTA (OCT-S1, Canon, Tokyo, Japan) images were obtained from 26 patients with DR. For each recording, approximately 20000 FA training data images were prepared from a 30-45-second FA video (Spectralis HRA, Heidelberg, Germany). AI-FA-like images were generated based on OCTA and the 20000 FA images from various time points using a convolutional neural network. Then, a generative adversarial network was adopted to make AI-FA images more similar to FA images/training data.
Results :
The similarity between the AI-FA like images (Fig1a), which were generated based on OCTA images (Fig1b), and the conventional FA images (Figure2) achieved an average structural similarity index measure of 0.91 (standard deviation: 0.08). Using AI-FA images, a FA video of corresponding time points was also successfully generated.
Conclusions :
With this novel AI-assisted OCTA system, high-resolution images of retinal vessels and their obstructed lesions, microaneurysms, and associated pathological leakage can be safely and clearly delineated without contrast agents.
This abstract was presented at the 2023 ARVO Imaging in the Eye Conference, held in New Orleans, LA, April 21-22, 2023.