ArticleEye (London, England)2025
Advancing AMD screening with an offline, AI-powered smartphone-based fundus camera: A prospective, real-world clinical validation.
Article in Eye (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Cost-effectiveness of community-based artificial intelligence screening for retinopathy of prematurity.Journal of global health · 2026Article
- 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
- Vision loss, disability and multimorbidity among older people in Andhra Pradesh and Telangana in South India: the longitudinal eye health, ageing and disability study (LEADS) - study protocol.BMJ public health · 2026Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study evaluated a novel offline, AI-driven age-related macular degeneration (AMD) screening algorithm against fundus image-only grading and the standard of care (combined Spectral Domain-Optical Coherence Tomography (SD-OCT) and fundus image grading).
methodsConducted prospectively at a South Asian tertiary eye hospital, this study utilized a validated smartphone-based non-mydriatic fundus camera to capture macula-centred images. The Medios AI's ability to detect referable AMD was compared to a reference standard image grading, using fundus images from the Zeiss Clarus 700 table-top camera and SD-OCT line scan across fovea. Three retina specialists provided blinded AMD diagnoses based on: (1) Zeiss Clarus 700 fundus images alone, and (2) combined SD-OCT and fundus images (standard of care). Referable AMD was defined as intermediate or advanced AMD.
resultsAmong 984 eyes from 492 patients (mean age 61.8 ± 9.9 years), 52% had referable AMD. Inter-grader agreement was strong, with Cohen's Kappa scores of 0.81-0.84. The Medios AI's sensitivity and specificity for detecting referable AMD against fundus-only grading (n = 492) were 88.48% (95% CI: 84.04-92.03%) and 87% (95% CI: 81.86-91.11%), respectively. Against combined grading (n = 489), AI sensitivity was 90.62% (95% CI: 86.37-93.90%), and specificity was 85.41% (95% CI: 80.21-89.68%). False negatives were primarily intermediate AMD (71%), while 59% of false positives were early AMD.
conclusionThe novel, automated, offline AMD AI integrated on a smartphone fundus camera demonstrated robust performance in identifying referable forms of AMD, supporting its potential as an affordable and accessible screening solution.
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