Retinal Detachment Screening with Ensembles of Neural Network Models

Abstract

Rhegmatogenous retinal detachment is an important condition that should be diagnosed early. A previous study showed that normal eyes and eyes with rhegmatogenous retinal detachment could be distinguished using pseudo-ocular fundus color images obtained with the Optos camera. However, no study has used pseudo-ocular fundus color images to distinguish eyes without retinal detachment (not necessarily normal) and those with rhegmatogenous retinal detachment. Furthermore, the previous study used a single neural network with only three layers. In the current study, we trained and validated an ensemble model of a deep neural networks involving ultra-wide-field pseudocolor images to distinguish non-retinal detachment eyes (not necessarily normal) and rhegmatogenous retinal detachment eyes. The study included 600 non-retinal detachment, 693 bullous rhegmatogenous retinal detachment, and 125 non-bullous rhegmatogenous retinal detachment images. The sensitivity and specificity of the ensemble model (five models) were 97.3% and 91.5%, respectively. In sum, this study demonstrated promising results for a screening system for rhegmatogenous retinal detachment with high sensitivity and relatively high specificity.