Artificial intelligence for diabetic retinopathy screening: a review

• Published in: Eye (London) Vol. 34; no. 3; pp. 451 - 460
• Main Authors: Grzybowski, Andrzej, Brona, Piotr, Lim, Gilbert, Ruamviboonsuk, Paisan, Tan, Gavin S. W., Abramoff, Michael, Ting, Daniel S. W.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.03.2020; Nature Publishing Group
• Subjects: 692/700/1421; 706/648/160; Aging; Algorithms; Artificial Intelligence; Deep learning; Diabetes; Diabetes Mellitus; Diabetic retinopathy; Diabetic Retinopathy - diagnosis; Diabetic Retinopathy - epidemiology; Humans; Laboratory Medicine; Learning algorithms; Machine Learning; Mass Screening; Medicine; Medicine & Public Health; Ophthalmology; Pharmaceutical Sciences/Technology; Retinopathy; Review; Review Article; Surgery; Surgical Oncology
• ISSN: 0950-222X; 1476-5454
• DOI: 10.1038/s41433-019-0566-0

Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing population, this poses significant challenge to perform diabetic retinopathy (DR) screening for these patients. Artificial intelligence (AI) using machine learning and deep learning have been adopted by various groups to develop automated DR detection algorithms. This article aims to describe the state-of-art AI DR screening technologies that have been described in the literature, some of which are already commercially available. All these technologies were designed using different training datasets and technical methodologies. Although many groups have published robust diagnostic performance of the AI algorithms for DR screening, future research is required to address several challenges, for examples medicolegal implications, ethics, and clinical deployment model in order to expedite the translation of these novel technologies into the healthcare setting.