ArticleInternational ophthalmology2023
EyeArt artificial intelligence analysis of diabetic retinopathy in retinal screening events.
Article in International ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- 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
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Real-World Performance of Artificial Intelligence in Diabetic Retinopathy Screening: A Systematic Review.Cureus · 2026Review
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- Global perspectives of ophthalmologists on artificial intelligence adoption in clinical practice.International journal of retina and vitreous · 2025Article
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- Comparison of Validity and Reliability of Manual Consensus Grading vs. Automated AI Grading for Diabetic Retinopathy Screening in Oslo, Norway: A Cross-Sectional Pilot Study.Journal of clinical medicine · 2025Article
- Two Handheld Retinograph Devices Evaluated by Ophthalmologists and an Artificial Intelligence Algorithm.Journal of clinical medicine · 2024Article
- The Use of Artificial Intelligence for Estimating Anterior Chamber Depth from Slit-Lamp Images Developed Using Anterior-Segment Optical Coherence Tomography.Bioengineering (Basel, Switzerland) · 2024Article
- Distribution of Microaneurysms and Hemorrhages in Accordance with the Grading of Diabetic Retinopathy in Type Diabetes Patients.Diagnostics (Basel, Switzerland) · 2024Article
- The application of artificial intelligence in diabetic retinopathy: progress and prospects.Frontiers in cell and developmental biology · 2024Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeEarly detection and treatment of diabetic retinopathy (DR) are critical for decreasing the risk of vision loss and preventing blindness. Community vision screenings may play an important role, especially in communities at higher risk for diabetes. To address the need for increased DR detection and referrals, we evaluated the use of artificial intelligence (AI) for screening DR.
methodsPatient images of 124 eyes were obtained using a 45° Canon Non-Mydriatic CR-2 Plus AF retinal camera in the Department of Endocrinology Clinic (Newark, NJ) and in a community screening event (Newark, NJ). Images were initially classified by an onsite grader and uploaded for analysis by EyeArt, a cloud-based AI software developed by Eyenuk (California, USA). The images were also graded by an off-site retina specialist. Using Fleiss kappa analysis, a correlation was investigated between the three grading systems, the AI, onsite grader, and a US board-certified retina specialist, for a diagnosis of DR and referral pattern.
resultsThe EyeArt results, onsite grader, and the retina specialist had a 79% overall agreement on the diagnosis of DR: 86 eyes with full agreement, 37 eyes with agreement between two graders, 1 eye with full disagreement. The kappa value for concordance on a diagnosis was 0.69 (95% CI 0.61-0.77), indicating substantial agreement. Referral patterns by EyeArt, the onsite grader, and the ophthalmologist had an 85% overall agreement: 96 eyes with full agreement, 28 eyes with disagreement. The kappa value for concordance on "whether to refer" was 0.70 (95% CI 0.60-0.80), indicating substantial agreement. Using the board-certified retina specialist as the gold standard, EyeArt had an 81% accuracy (101/124 eyes) for diagnosis and 83% accuracy (103/124 eyes) in referrals. For referrals, the sensitivity of EyeArt was 74%, specificity was 87%, positive predictive value was 72%, and negative predictive value was 88%.
conclusionsThis retrospective cross-sectional analysis offers insights into use of AI in diabetic screenings and the significant role it will play in automated detection of DR. The EyeArt readings were beneficial with some limitations in a community screening environment. These limitations included a decreased accuracy in the presence of cataracts and the functional cost of EyeArt uploads in a community setting.
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