Performance and Clinical Utility of Deep Learning for Detecting Referable Age-Related Macular Degeneration on Fundus Photographs: A Systematic Review and Meta-Analysis

TL;DR

This paper reviews how well deep learning can detect serious age-related eye disease from photos and finds it performs well compared to human experts.

Contribution

The study provides a systematic review and meta-analysis of deep learning's diagnostic accuracy for referable AMD detection using fundus photographs.

Findings

DL algorithms showed high pooled sensitivity (0.91) and specificity (0.93) for detecting referable AMD.

DL systems had higher specificity than human graders but slightly lower sensitivity.

The pooled positive and negative likelihood ratios suggest strong diagnostic utility of DL.

Abstract

Background/Objectives: Age-related macular degeneration (AMD) is a leading cause of irreversible central vision loss in older adults. Detection of referable AMD—typically intermediate or advanced disease requiring specialist evaluation—is critical for timely intervention. Deep learning (DL) applied to color fundus photographs has emerged as a potential tool to support large-scale AMD screening. This systematic review and meta-analysis evaluated the diagnostic accuracy of DL algorithms for detecting referable AMD and compared their performance with human graders. Methods: We systematically searched PubMed, Embase, Web of Science, and IEEE Xplore through 18 December 2025. Diagnostic accuracy studies assessing DL algorithms on color fundus photographs for referable AMD in adults were included. Two reviewers independently screened studies, extracted data, and assessed risk of bias using an AI-adapted PROBAST framework. Pooled sensitivity and specificity were estimated using a bivariate random-effects model. Clinical utility was evaluated using likelihood ratios, and paired head-to-head comparisons were synthesized using a contrast-based meta-analysis. Results: Fourteen studies were included. DL algorithms achieved a pooled sensitivity of 0.91 (95% CI: 0.86–0.94) and specificity of 0.93 (95% CI: 0.86–0.96), with substantial heterogeneity. The pooled positive and negative likelihood ratios were 12.22 and 0.10, respectively, indicating strong diagnostic utility. In direct comparisons, DL systems showed slightly lower sensitivity but higher specificity than human graders. Conclusions: Deep learning demonstrates high diagnostic accuracy for detecting referable AMD from fundus photographs and may support screening and referral workflows. Further prospective validation and standardized evaluation are needed before widespread clinical implementation.

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