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.
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Masumoto, H., Tabuchi, H., Adachi, S., Nakakura, S., Ohsugi, H., Nagasato, D. (2019). Retinal Detachment Screening with Ensembles of Neural Network Models. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_20
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