Computer aided diabetic retinopathy detection based on ophthalmic photography: a systematic review and Meta-analysis
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Ai-Min Sang. Department of Ophthalmology, Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China. sangam@ntu.edu.cn; Jian-Cheng Dong. Department of Medical Informatics, Medical School of Nantong University, Nantong 226001, Jiangsu Province, China. dongjc@ntu.edu.cn

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Supported by National Key R&D Program of China (No.2018YFC1314900; No.2018YFC1314902); Nantong “226 Project”, Excellent Key Teachers in the “Qing Lan Project” of Jiangsu Colleges and Universities, Jiangsu Students’ Platform for Innovation and Entrepreneurship Training Program (No.201910304108Y).

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    Abstract:

    AIM: To ensure the diagnostic value of computer aided techniques in diabetic retinopathy (DR) detection based on ophthalmic photography (OP). METHODS: PubMed, EMBASE, Ei village, IEEE Xplore and Cochrane Library database were searched systematically for literatures about computer aided detection (CAD) in DR detection. The methodological quality of included studies was appraised by the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2). Meta-DiSc was utilized and a random effects model was plotted to summarize data from those included studies. Summary receiver operating characteristic curves were selected to estimate the overall test performance. Subgroup analysis was used to identify the efficiency of CAD in detecting DR, exudates (EXs), microaneurysms (MAs) as well as hemorrhages (HMs), and neovascularizations (NVs). Publication bias was analyzed using STATA. RESULTS: Fourteen articles were finally included in this Meta-analysis after literature review. Pooled sensitivity and specificity were 90% (95%CI, 85%-94%) and 90% (95%CI, 80%-96%) respectively for CAD in DR detection. With regard to CAD in EXs detecting, pooled sensitivity, specificity were 89% (95%CI, 88%-90%) and 99% (95%CI, 99%-99%) respectively. In aspect of MAs and HMs detection, pooled sensitivity and specificity of CAD were 42% (95%CI, 41%-44%) and 93% (95%CI, 93%-93%) respectively. Besides, pooled sensitivity and specificity were 94% (95%CI, 89%-97%) and 87% (95%CI, 83%-90%) respectively for CAD in NVs detection. No potential publication bias was observed. CONCLUSION: CAD demonstrates overall high diagnostic accuracy for detecting DR and pathological lesions based on OP. Further prospective clinical trials are needed to prove such effect.

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Wu HQ, Shan YX, Wu H, Zhu DR, Tao HM, Wei HG, Shen XY, Sang AM, Dong JC. Computer aided diabetic retinopathy detection based on ophthalmic photography: a systematic review and Meta-analysis. Int J Ophthalmol 2019;12(12):1908-1916

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Publication History
  • Received:April 25,2019
  • Revised:June 10,2019
  • Adopted:
  • Online: November 06,2019
  • Published: