Deep Learning Algorithms for Detection of Diabetic Macular Edema in OCT Images: A Systematic Review and Meta-Analysis

Abstract

Purpose: Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection. Methods: Embase, Pubmed, the Cochrane Library, and IEEE Xplore were searched up to November 14, 2020. We included studies used deep learning algorithms to detect DME from OCT images. Two reviewers extracted the data independently, and QUDAS-2 tool was applied to assess the risk of bias. Results: Eighteen studies involving 41005 subjects were included. The pooled sensitivity and specificity was 96.0% (95% confidence interval (CI): 93.9% to 97.3%) and 99.3% (95% CI: 98.2% to 99.7%), respectively. The pooled diagnostic odds (DOR) was 3491.0 (95% CI: 1117.2 to 10908.7). The positive likelihood ratio (PLR) and negative likelihood (NLR) was 142.2 (95% CI: 54.2 to 373.5) and 0.04 (95% CI: 0.03 to 0.06), respectively. Subgroup analyses found that data set selection, sample size of training set and the choice of OCT devices contributed to the heterogeneity(all P 0.05). Conclusions: Deep learning methods could effectively detect clinically significant DME. Funding Statement: This study was supported by The Capital Health Research and Development of Special (2020-1-2052); Science & Technology Project of Beijing Municipal Science & Technology Commission (Z201100005520045, Z181100001818003); the Beijing Municipal Administration of Hospitals’ Ascent Plan (DFL20150201). Declaration of Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.