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

•Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Detecting and treating DR at an earlier stage is desirable to reduce the incidence and progression of visual loss.•A systematic review with a meta-analysis of relevant studies was performed to quantify the performance of...

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Published in:Computer methods and programs in biomedicine 2020-07, Vol.191, p.105320-105320, Article 105320
Main Authors: Islam, Md Mohaimenul, Yang, Hsuan-Chia, Poly, Tahmina Nasrin, Jian, Wen-Shan, (Jack) Li, Yu-Chuan
Format: Article
Language:English
Subjects:
Algorithms
Deep Learning
Diabetic
Diabetic retinopathy
Diabetic Retinopathy - diagnosis
Diagnostic Techniques, Ophthalmological
Fundus photograph
Humans
Mass Screening - methods
Retinal Vessels - diagnostic imaging
Retinopathy
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Summary:•Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Detecting and treating DR at an earlier stage is desirable to reduce the incidence and progression of visual loss.•A systematic review with a meta-analysis of relevant studies was performed to quantify the performance of DL algorithms to detect DR.•The pooled area under the receiving operating curve (AUROC) of DR was 0.97 (95%CI: 0.95–0.98), sensitivity was 0.83 (95%CI: 0.83–0.83), and specificity was 0.92 (95%CI: 0.92–0.92).•The findings of our study showed that DL-algorithms had high sensitivity and specificity for detecting referable DR from retinal fundus photographs. Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Earlier detection and timely treatment of DR are desirable to reduce the incidence and progression of vision loss. Currently, deep learning (DL) approaches have offered better performance in detecting DR from retinal fundus images. We, therefore, performed a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms for detecting DR. A systematic literature search on EMBASE, PubMed, Google Scholar, Scopus was performed between January 1, 2000, and March 31, 2019. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and DL-based study design was mandatory for articles inclusion. Two independent authors screened abstracts and titles against inclusion and exclusion criteria. Data were extracted by two authors independently using a standard form and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used for the risk of bias and applicability assessment. Twenty-three studies were included in the systematic review; 20 studies met inclusion criteria for the meta-analysis. The pooled area under the receiving operating curve (AUROC) of DR was 0.97 (95%CI: 0.95–0.98), sensitivity was 0.83 (95%CI: 0.83–0.83), and specificity was 0.92 (95%CI: 0.92–0.92). The positive- and negative-likelihood ratio were 14.11 (95%CI: 9.91–20.07), and 0.10 (95%CI: 0.07–0.16), respectively. Moreover, the diagnostic odds ratio for DL models was 136.83 (95%CI: 79.03–236.93). All the studies provided a DR-grading scale, a human grader (e.g. trained caregivers, ophthalmologists) as a reference standard. The findings of our study showed that DL algorithms had high sensitivity and specificity for detecting referable DR from
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2020.105320