Literature DB >> 33010796

Progress of artificial intelligence in diabetic retinopathy screening.

Yue-Lin Wang1,2, Jing-Yun Yang3,4, Jing-Yuan Yang1,2, Xin-Yu Zhao1,2, You-Xin Chen1,2, Wei-Hong Yu1,2.   

Abstract

Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide, and the limited availability of qualified ophthalmologists restricts its early diagnosis. For the past few years, artificial intelligence technology has developed rapidly and has been applied in DR screening. The upcoming technology provides support on DR screening and improves the identification of DR lesions with a high sensitivity and specificity. This review aims to summarize the progress on automatic detection and classification models for the diagnosis of DR.
© 2020 John Wiley & Sons Ltd.

Entities:  

Keywords:  artificial intelligence; deep learning; diabetic retinopathy; progress; review; screening

Mesh:

Year:  2020        PMID: 33010796     DOI: 10.1002/dmrr.3414

Source DB:  PubMed          Journal:  Diabetes Metab Res Rev        ISSN: 1520-7552            Impact factor:   4.876


  3 in total

1.  The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy.

Authors:  Wen-Fei Zhang; Dong-Hong Li; Qi-Jie Wei; Da-Yong Ding; Li-Hui Meng; Yue-Lin Wang; Xin-Yu Zhao; You-Xin Chen
Journal:  Front Med (Lausanne)       Date:  2022-05-16

2.  Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology.

Authors:  Yang Xiang; Lai Shujin; Chang Hongfang; Wen Yinping; Yu Dawei; Dong Zhou; Li Zhiqing
Journal:  Biomed Res Int       Date:  2021-04-20       Impact factor: 3.411

3.  Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Authors:  Frank Ursin; Cristian Timmermann; Marcin Orzechowski; Florian Steger
Journal:  Front Med (Lausanne)       Date:  2021-07-21
  3 in total

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