Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis

2020 ◽  
Vol 191 ◽  
pp. 105320 ◽  
Author(s):  
Md Mohaimenul Islam ◽  
Hsuan-Chia Yang ◽  
Tahmina Nasrin Poly ◽  
Wen-Shan Jian ◽  
Yu-Chuan (Jack) Li
Keyword(s):  
Systematic Review ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Fundus Photographs ◽  
Retinal Fundus

scholarly journals Assessing the Performance of Artificial Intelligence Systems for the Screening of Diabetic Retinopathy: A Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Ryan Sadjadi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Meta Analysis ◽  
External Validation ◽  
Healthcare Providers ◽  
Vitreoretinal Surgery ◽  
Retinal Fundus ◽  
Screening Systems

Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and one of the leading causes of blindness globally. Due to the progressive nature of the disease, earlier detection and timely treatment can lead to substantial reductions in the incidence of irreversible vision-loss. Artificial intelligence (AI) screening systems have offered clinically acceptable and quicker results in detecting diabetic retinopathy from retinal fundus and optical coherence tomography (OCT) images. Thus, this systematic review and meta-analysis of relevant investigations was performed to document the performance of AI screening systems that were applied to fundus and OCT images of patients from diverse geographic locations including North America, Europe, Africa, Asia, and Australia. A systematic literature search on Medline, Global Health, and PubMed was performed and studies published between October 2015 and January 2020 were included. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and AI-based investigations were mandatory for studies inclusion. The abstracts, titles, and full-texts of potentially eligible studies were screened against inclusion and exclusion criteria. Twenty-one studies were included in this systematic review; 18 met inclusion criteria for the meta-analysis. The pooled sensitivity of the evaluated AI screening systems in detecting diabetic retinopathy was 0.93 (95% CI: 0.92-0.94) and the specificity was 0.88 (95% CI: 0.86-0.89). The included studies detailed training and external validation datasets, criteria for diabetic retinopathy case ascertainment, imaging modalities, DR-grading scales, and compared AI results to those of human graders (e.g., ophthalmologists, retinal specialists, trained nurses, and other healthcare providers) as a reference standard. The findings of this study showed that the majority AI screening systems demonstrated clinically acceptable levels of sensitivity and specificity for detecting referable diabetic retinopathy from retinal fundus and OCT photographs. Further improvement depends on the continual development of novel algorithms with large and gradable sets of images for training and validation. If cost-effectiveness ratios can be optimized, AI can become a financially sustainable and clinically effective intervention that can be incorporated into the healthcare systems of low-to-middle income countries (LMICs) and geographically remote locations. Combining screening technologies with treatment interventions such as anti-VEGF therapy, acellular capillary laser treatment, and vitreoretinal surgery can lead to substantial reductions in the incidence of irreversible vision-loss due to proliferative diabetic retinopathy.

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

JAMA ◽  
2016 ◽  
Vol 316 (22) ◽  
pp. 2402 ◽  
Author(s):  
Varun Gulshan ◽  
Lily Peng ◽  
Marc Coram ◽  
Martin C. Stumpe ◽  
Derek Wu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Fundus Photographs ◽  
Development And Validation ◽  
Retinal Fundus
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