Artificial intelligence for diabetic retinopathy screening: a review

A Correction to this article was published on 10 December 2019

This article has been updated

Abstract

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.

摘要:

糖尿病为全球性的眼科健康问题, 随着糖尿病患病率的增加以及人口老龄化, 对糖尿病患者进行糖尿病视网膜病变(DR)的筛查已成为重大挑战。很多团队采用了人工智能(AI)的方法, 利用机器学习(ML)和深度学习(DL)技术开发自动检测DR的算法。本文介绍了现已报道的最先进的AI筛查DR技术, 其中一些已经商业化。这些技术都是利用不同的训练数据集和技术方法设计的。尽管许多团队的研究表明AI算法用于筛查DR具有强大的诊断性能, 但未来的研究仍面临诸多挑战, 例如对法医学的影响, 加速这些新技术向医疗机构转化所涉及的伦理学和临床部署模式等等。

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Change history

  • 10 December 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Correspondence to Daniel S. W. Ting.

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The authors declare that they have no conflict of interest. GL and DT are the co-inventors of a deep learning system for retinal diseases. MA is the inventor on patents and patent applications of artificial intelligence and machine learning algorithms for diagnosis and treatment. He is a Founder CEO, employee, of and investor in IDx, Coralville, Iowa, USA.

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Grzybowski, A., Brona, P., Lim, G. et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye 34, 451–460 (2020). https://doi.org/10.1038/s41433-019-0566-0

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