ArticleActa ophthalmologica2018
Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System.
Article in Acta ophthalmologica, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 136 papers, 2 of them syntheses that pooled it.
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Who cites it
136 citing papers in PubMed, 2 syntheses or guidelines pooled it, 290 citations in OpenAlex.
- Trends and hotspots in the field of diabetic retinopathy imaging research from 2000-2023.Frontiers in medicine · 2024Pooled it
- Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review.Journal of medical screening · 2023Pooled it
- Trial
- The diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care: a prospective validation study.Scandinavian journal of primary health care · 2026Article
- AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application.World journal of methodology · 2026Review
- Deep Learning Prediction of Retinal Thickness from Near-Infrared Fundus Photography: Toward Decentralized Quantitative Assessment of Diabetic Macular Edema.Journal of personalized medicine · 2026Article
- AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images.Translational vision science & technology · 2026Article
- Review
- Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.Translational vision science & technology · 2026Article
- Prospective Data Curation Enables High-Performance Artificial Intelligence for Diabetic Retinopathy Screening in a Resource-Limited Setting.Ophthalmology science · 2026Article
- Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives.Health care science · 2026Review
- Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Diabetic Retinopathy Detection: AI Models and Approaches.Journal of ophthalmology · 2026Review
- Article
- Multimodal Deep Learning for Diabetic Retinopathy: A Survey.Journal of ophthalmology · 2026Review
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- Artificial Intelligence Driven Innovation: Advancing Mesenchymal Stem Cell Therapies and Intelligent Biomaterials for Regenerative Medicine.Bioengineering (Basel, Switzerland) · 2025Review
76 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo increase the efficiency of retinal image grading, algorithms for automated grading have been developed, such as the IDx-DR 2.0 device. We aimed to determine the ability of this device, incorporated in clinical work flow, to detect retinopathy in persons with type 2 diabetes.
methodsRetinal images of persons treated by the Hoorn Diabetes Care System (DCS) were graded by the IDx-DR device and independently by three retinal specialists using the International Clinical Diabetic Retinopathy severity scale (ICDR) and EURODIAB criteria. Agreement between specialists was calculated. Results of the IDx-DR device and experts were compared using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), distinguishing between referable diabetic retinopathy (RDR) and vision-threatening retinopathy (VTDR). Area under the receiver operating characteristic curve (AUC) was calculated.
resultsOf the included 1415 persons, 898 (63.5%) had images of sufficient quality according to the experts and the IDx-DR device. Referable diabetic retinopathy (RDR) was diagnosed in 22 persons (2.4%) using EURODIAB and 73 persons (8.1%) using ICDR classification. Specific intergrader agreement ranged from 40% to 61%. Sensitivity, specificity, PPV and NPV of IDx-DR to detect RDR were 91% (95% CI: 0.69-0.98), 84% (95% CI: 0.81-0.86), 12% (95% CI: 0.08-0.18) and 100% (95% CI: 0.99-1.00; EURODIAB) and 68% (95% CI: 0.56-0.79), 86% (95% CI: 0.84-0.88), 30% (95% CI: 0.24-0.38) and 97% (95% CI: 0.95-0.98; ICDR). The AUC was 0.94 (95% CI: 0.88-1.00; EURODIAB) and 0.87 (95% CI: 0.83-0.92; ICDR). For detection of VTDR, sensitivity was lower and specificity was higher compared to RDR. AUC's were comparable.
conclusionAutomated grading using the IDx-DR device for RDR detection is a valid method and can be used in primary care, decreasing the demand on ophthalmologists.
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