ArticleBMJ open2024
Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Strategies for strengthening eye care in India: a consensus-based recommendations.The Lancet regional health. Southeast Asia · 2026Review
- Performance and Clinical Utility of Deep Learning for Detecting Referable Age-Related Macular Degeneration on Fundus Photographs: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Explainable multimodal AI and neuro-symbolic clinical decision support system for chronic eye disease management: a digital health implementation study.Frontiers in digital health · 2026Article
- Artificial intelligence for precision therapeutics in age-related macular degeneration: current advances, challenges, and future directions.Frontiers in artificial intelligence · 2026Review
- Explainable Artificial Intelligence Framework for Predicting Treatment Outcomes in Age-Related Macular Degeneration.Sensors (Basel, Switzerland) · 2025Article
- Advancing AMD screening with an offline, AI-powered smartphone-based fundus camera: A prospective, real-world clinical validation.Eye (London, England) · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
objectivesDespite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however, to our knowledge, such an artificial intelligence (AI) system has not been evaluated. The study aimed to assess the performance of an AI algorithm in detecting referable AMD on images captured on a portable fundus camera. DESIGN,
settingA retrospective image database from the Age-Related Eye Disease Study (AREDS) and target device was used.
participantsThe algorithm was trained on two distinct data sets with macula-centric images: initially on 108,251 images (55% referable AMD) from AREDS and then fine-tuned on 1108 images (33% referable AMD) captured on Asian eyes using the target device. The model was designed to indicate the presence of referable AMD (intermediate and advanced AMD). Following the first training step, the test set consisted of 909 images (49% referable AMD). For the fine-tuning step, the test set consisted of 238 (34% referable AMD) images. The reference standard for the AREDS data set was fundus image grading by the central reading centre, and for the target device, it was consensus image grading by specialists. OUTCOME MEASURES: Area under receiver operating curve (AUC), sensitivity and specificity of algorithm.
resultsBefore fine-tuning, the deep learning (DL) algorithm exhibited a test set (from AREDS) sensitivity of 93.48% (95% CI: 90.8% to 95.6%), specificity of 82.33% (95% CI: 78.6% to 85.7%) and AUC of 0.965 (95% CI:0.95 to 0.98). After fine-tuning, the DL algorithm displayed a test set (from the target device) sensitivity of 91.25% (95% CI: 82.8% to 96.4%), specificity of 84.18% (95% CI: 77.5% to 89.5%) and AUC 0.947 (95% CI: 0.911 to 0.982).
conclusionThe DL algorithm shows promising results in detecting referable AMD from a portable smartphone-based imaging system. This approach can potentially bring effective and affordable AMD screening to underserved areas.
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