The following article is a part of conference coverage from the American Academy of Ophthalmology 2020, being held virtually from November 13 to 15, 2020. The team at Ophthalmology Advisor will be reporting on the latest news and research conducted by leading experts in ophthalmology. Check back for more from the AAO 2020.

Images obtained from optical coherence tomography angiography (OCT-A) can be evaluated by a deep convolutional neural network, according to research presented at the American Academy of Ophthalmology 2020 meeting, held virtually November 13 to 15. Deep convolutional neural networks are a deep learning algorithm that can accurately identify and classify images, in this case, of diabetic retinopathy and its severity. 

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Researchers tested this technology using 2489 OCT-A images from 1163 eyes (857 eyes with diabetes and 306 controls). A deep convolutional neural network was trained to classify diabetic retinopathy stages using superficial, deep, and whole retinal layer images. Data were divided into 4 subsets, on which investigators performed 4-fold cross-validation. Neural network outputs were compared with the outputs from both retinal experts and traditional machine learning methods. 

Within the convolutional neural network, investigators identified an accuracy of 85% for 6-level staging tasks, compared with 53% accuracy of traditional machine learning methods and 31% accuracy of retinal experts. 

“The deep learning-based classification using OCT-A images can provide clinicians with a classification of [diabetic retinopathy] severity for patients with diabetes,” the researchers concluded. 

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Sagong M, Cha S. A deep convolutional neural network for classification of DR using OCT-A. Presented at: American Academy of Ophthalmology 2020 Annual Meeting; November 13-15, 2020. Abstract PO400.