Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections

To clarify the real-world performance of regulator-approved deep-learning (DL) systems for autonomous diabetic retinopathy (DR) screening, we systematically searched PubMed, Embase, and ClinicalTrials.gov to 3 April 2025, identifying 82 studies (887,244 examinations) covering 25 devices in 28 countries. Hierarchical bivariate meta-analysis yielded pooled sensitivity/specificity of 0.93/0.90 on a per-patient basis and 0.92/0.93 per eye, closely paralleling expert grading. Meta-regression showed that DR severity threshold, national-income level, image gradability, pupil dilation, reference standard, and diagnostic criteria collectively explained most between-study heterogeneity; any-DR screening, low-income settings, or ungradable images increased false-positive rates, whereas dilated pupils, portable cameras, and adjudicated references improved specificity. Publication bias was minimal. Overall, regulator-approved DL algorithms provide accurate, scalable DR detection, but programs must tailor deployment and reimbursement to disease threshold, image quality, and local resources, and post-market audits with standardized gradability metrics are needed to ensure safe, equitable global adoption.