ArticleEye (London, England)2018
Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence.
Article in Eye (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 173 papers, 9 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
173 citing papers in PubMed, 9 syntheses or guidelines pooled it, 442 citations in OpenAlex.
- Comparative diagnostic accuracy of photographic methods for detecting diabetic retinopathy: A systematic review and meta-analysis.European journal of ophthalmology · 2026Pooled it
- Effectiveness of screening modalities for early detection of diabetic retinopathy: a systematic review and meta-analysis of tele-ophthalmology, AI-based tools, and conventional methods.Frontiers in medicine · 2026Pooled it
- Diabetic Retinopathy Screening Approaches in Developing Countries: A Systematic Review and Meta-Analysis.Turkish journal of ophthalmology · 2025Pooled it
- Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.Biomedical engineering online · 2025Pooled it
- Artificial intelligence versus manual screening for the detection of diabetic retinopathy: a comparative systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis.Frontiers in endocrinology · 2025Pooled it
- Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.Journal of medical Internet research · 2021Pooled it
- Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies.Journal of medical Internet research · 2020Pooled it
- Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.Current diabetes reports · 2019Pooled it
- Trial
- Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images.Frontiers in artificial intelligence · 2026Trial
- Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial.Nature communications · 2024Trial
- SEFCA-Net: a semantic-edge-frequency cross-attention network for diabetic retinopathy lesion segmentation.Journal of biomedical optics · 2026Article
- A hybrid model for early diagnosis of ophthalmology diseases leveraging CNNs, SBOA optimization, and XAI for visualization.Physical and engineering sciences in medicine · 2026Article
- Impacting lives of people with diabetes: lessons learnt in India may be applicable to other low- and middle-income countries.Diabetologia · 2026Review
- Leading Towards a Translation Readiness Framework: A Systematic Review of Deep Learning Approaches for Eye Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Teleophthalmology for Retinal Disease Screening and Referral in Low- and Middle-Income Countries: A Case Overview of India.International ophthalmology · 2026Review
- Review
- A Roadmap for National Diabetic Retinopathy Screening in Croatia: Integrating European Evidence, Telemedicine, and AI.Medicina (Kaunas, Lithuania) · 2026Review
- OrdPrune-KD: An Ordinal-Consistency-Based Model Compression Framework for Diabetic Retinopathy Grading.Sensors (Basel, Switzerland) · 2026Article
113 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess the role of artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) by fundus photography taken using a smartphone-based device and validate it against ophthalmologist's grading.
methodsThree hundred and one patients with type 2 diabetes underwent retinal photography with Remidio 'Fundus on phone' (FOP), a smartphone-based device, at a tertiary care diabetes centre in India. Grading of DR was performed by the ophthalmologists using International Clinical DR (ICDR) classification scale. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The retinal photographs were graded using a validated AI DR screening software (EyeArt
resultsRetinal images of 296 patients were graded. DR was detected by the ophthalmologists in 191 (64.5%) and by the AI software in 203 (68.6%) patients while STDR was detected in 112 (37.8%) and 146 (49.3%) patients, respectively. The AI software showed 95.8% (95% CI 92.9-98.7) sensitivity and 80.2% (95% CI 72.6-87.8) specificity for detecting any DR and 99.1% (95% CI 95.1-99.9) sensitivity and 80.4% (95% CI 73.9-85.9) specificity in detecting STDR with a kappa agreement of k = 0.78 (p < 0.001) and k = 0.75 (p < 0.001), respectively.
conclusionsAutomated AI analysis of FOP smartphone retinal imaging has very high sensitivity for detecting DR and STDR and thus can be an initial tool for mass retinal screening in people with diabetes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.