Artificial Intelligence Using Deep Learning in Classifying Side of the Eyes and Width of Field for Retinal Fundus Photographs

Abstract

As the application of deep learning (DL) advances in the healthcare sector, the need for simultaneous, multi-annotated database of medical images for evaluations of novel DL systems grows. This study looked at DL algorithms that distinguish retinal images by the side of the eyes (Left and Right side) as well as the field positioning (Macular-centred or Optic Disc-centred) and evaluated these algorithms against a large dataset comprised of 7,953 images from multi-ethnic populations. For these convolutional neural networks, L/R model and Mac/OD model, a high AUC (0.978, 0.990), sensitivity (95.9%, 97.6%), specificity (95.5%, 96.7%) and accuracy (95.7%, 97.2%) were found, respectively, for the primary validation sets. The models presented high performance also using the external validation database.