期刊
CURRENT DIABETES REPORTS
卷 19, 期 9, 页码 -出版社
CURRENT MEDICINE GROUP
DOI: 10.1007/s11892-019-1189-3
关键词
Artificial intelligence; Deep learning; Diabetic retinopathy screening; Retinal images; Tele-medicine; Survey
资金
- Research Grants Council-General Research Fund, Hong Kong [14102418]
- National Medical Research Council Health Service Research Grant, Large Collaborative Grant, Ministry of Health, Singapore
- SingHealth Foundation
- Tanoto Foundation
Purpose of ReviewThis paper systematically reviews the recent progress in diabetic retinopathy screening. It provides an integrated overview of the current state of knowledge of emerging techniques using artificial intelligence integration in national screening programs around the world. Existing methodological approaches and research insights are evaluated. An understanding of existing gaps and future directions is created.Recent FindingsOver the past decades, artificial intelligence has emerged into the scientific consciousness with breakthroughs that are sparking increasing interest among computer science and medical communities. Specifically, machine learning and deep learning (a subtype of machine learning) applications of artificial intelligence are spreading into areas that previously were thought to be only the purview of humans, and a number of applications in ophthalmology field have been explored. Multiple studies all around the world have demonstrated that such systems can behave on par with clinical experts with robust diagnostic performance in diabetic retinopathy diagnosis. However, only few tools have been evaluated in clinical prospective studies.SummaryGiven the rapid and impressive progress of artificial intelligence technologies, the implementation of deep learning systems into routinely practiced diabetic retinopathy screening could represent a cost-effective alternative to help reduce the incidence of preventable blindness around the world.
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