ArticleJournal of diabetes science and technology2022
A Comparison of Artificial Intelligence and Human Diabetic Retinal Image Interpretation in an Urban Health System.
Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled 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
- The diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care: a prospective validation study.Scandinavian journal of primary health care · 2026Article
- Classification of Inherited Retinal Diseases Using Artificial Intelligence Models for Fundus Autofluorescence and Ultrawide Retinal Images.Journal of ophthalmology · 2026Article
- Autonomous Artificial Intelligence in Diabetic Retinopathy Testing-Lessons Learned on Successful Health System Adoption.Ophthalmology science · 2026Article
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- Evaluation of the degree of agreement in the diagnosis of diabetic retinopathy between ophthalmologists and EyeArtInternational journal of retina and vitreous · 2025Article
- 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
- Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study.Journal of diabetes science and technology · 2025Article
- Recommendations for the optimization of student led free vision screening programs.BMC medical education · 2024Article
- Opportunities to Apply Human-centered Design in Health Care With Artificial Intelligence-based Screening for Diabetic Retinopathy.International ophthalmology clinics · 2024Review
- Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.Eye (London, England) · 2024Article
- Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis.PLOS global public health · 2023Article
- Multi-label classification of retinal disease via a novel vision transformer model.Frontiers in neuroscience · 2023Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial intelligence (AI) diabetic retinopathy (DR) software has the potential to decrease time spent by clinicians on image interpretation and expand the scope of DR screening. We performed a retrospective review to compare Eyenuk's EyeArt software (Woodland Hills, CA) to Temple Ophthalmology optometry grading using the International Classification of Diabetic Retinopathy scale.
methodsTwo hundred and sixty consecutive diabetic patients from the Temple Faculty Practice Internal Medicine clinic underwent 2-field retinal imaging. Classifications of the images by the software and optometrist were analyzed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and McNemar's test. Ungradable images were analyzed to identify relationships with HbA1c, age, and ethnicity. Disagreements and a sample of 20% of agreements were adjudicated by a retina specialist.
resultsOn patient level comparison, sensitivity for the software was 100%, while specificity was 77.78%. PPV was 19.15%, and NPV was 100%. The 38 disagreements between software and optometrist occurred when the optometrist classified a patient's images as non-referable while the software classified them as referable. Of these disagreements, a retina specialist agreed with the optometrist 57.9% the time (22/38). Of the agreements, the retina specialist agreed with both the program and the optometrist 96.7% of the time (28/29). There was a significant difference in numbers of ungradable photos in older patients (≥60) vs younger patients (<60) (p=0.003).
conclusionsThe AI program showed high sensitivity with acceptable specificity for a screening algorithm. The high NPV indicates that the software is unlikely to miss DR but may refer patients unnecessarily.
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