Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
School of Computing, National University of Singapore, Singapore, Singapore.
Curr Diab Rep. 2019 Jul 31;19(9):72. doi: 10.1007/s11892-019-1189-3.
This 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.
Over 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. Given 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.
本文系统地回顾了糖尿病视网膜病变筛查的最新进展。它综合概述了目前利用人工智能整合在全球各国筛查计划中的新兴技术的知识现状。评估了现有的方法方法和研究见解。创建了对现有差距和未来方向的理解。
在过去的几十年中，人工智能已经进入了科学界的意识，其突破引起了计算机科学界和医学界越来越多的兴趣。具体来说，人工智能的机器学习和深度学习（机器学习的一个子类）应用正在扩展到以前被认为仅属于人类领域的领域，并且已经探索了眼科领域的许多应用。世界各地的多项研究都表明，这些系统可以与临床专家一样表现出色，在糖尿病视网膜病变诊断方面具有强大的诊断性能。但是，只有少数工具在临床前瞻性研究中进行了评估。鉴于人工智能技术的快速和令人印象深刻的进展，将深度学习系统纳入常规实践的糖尿病视网膜病变筛查可能是一种具有成本效益的替代方法，可以帮助减少全球可预防失明的发生率。
