Development and validation of a deep learning algorithm for distinguishing the nonperfusion area from signal reduction artifacts on OCT angiography

The capillary nonperfusion area (NPA) is a key quantifiable biomarker in the evaluation of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). HOWeVer, signal reduction artifacts caused by vitreous floaters, pupil vignetting, or defocus present significant obstacles to accurate quantification. We have developed a convolutional neural network, MEDnet-V2, to distinguish NPA from signal reduction artifacts in 6x6 mm(2) OCTA. The network achieves strong specificity and sensitivity for NPA detection across a wide range of DR severity and scan quality. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement