Published on Thu Sep 16 2021

Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study.

Vincent Yuen, Anran Ran, Jian Shi, Kaiser Sham, Dawei Yang, Victor T T Chan, Raymond Chan, Jason C Yam, Clement C Tham, Gareth J McKay, Michael A Williams, Leopold Schmetterer, Ching-Yu Cheng, Vincent Mok, Christopher L Chen, Tien Y Wong, Carol Y Cheung
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Abstract

Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left).