ArticleInternational journal of ophthalmology2022
Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos-a prospective analysis of the RetCAD system.
Article in International journal of ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Performance of Deep Learning in Classifying Age-Related Macular Degeneration From Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Why aren't we using AI in eye clinics? A systematic review of barriers and solutions in AI-based fundus image diagnostics for ocular diseases.International journal of ophthalmology · 2026Review
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- Advancing AMD screening with an offline, AI-powered smartphone-based fundus camera: A prospective, real-world clinical validation.Eye (London, England) · 2025Article
- Automated Diabetic Retinopathy Screening in Out-patient Diabetes Care - Comparison of Two Artificial Intelligence Algorithms: RetCAD and OphtAI.Klinische Monatsblatter fur Augenheilkunde · 2025Article
- Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Software: IDx-DR and RetCAD.Ophthalmology and therapy · 2025Article
- Improving diabetic retinopathy screening using artificial intelligence: design, evaluation and before-and-after study of a custom development.Frontiers in digital health · 2025Article
- Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera.BMJ open · 2024Article
- Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.Survey of ophthalmologyReview
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo assess the accuracy of an artificial intelligence (AI) based software (RetCAD, Thirona, The Netherlands) to identify and grade age-related macular degeneration (AMD) and diabetic retinopathy (DR) simultaneously based on fundus photos.
methodsThis prospective study included 1245 eyes of 630 patients attending an ophthalmology day-care clinic. Fundus photos were acquired and parallel graded by the RetCAD AI software and by an expert reference examiner for image quality, and staging of AMD and DR. Adjudication was provided by a second expert examiner in case of disagreement between the AI software and the reference examiner. Statistical analysis was performed on eye-level and on patient-level, by summarizing the individual image level-gradings into and eye-level or patient-level score, respectively. The performance of the RetCAD system was measured using receiver operating characteristics (ROC) analysis and sensitivity and specificity for both AMD and DR were reported.
resultsThe RetCAD achieved an area under the ROC (Az) of 0.926 with a sensitivity of 84.6% at a specificity of 84.0% for image quality. On image level, the RetCAD software achieved Az values of 0.964 and 0.961 with sensitivity/specificity pairs of 98.2%/79.1% and 83.9%/93.3% for AMD and DR, respectively. On patient level, the RetCAD software achieved Az values of 0.960 and 0.948 with sensitivity/specificity pairs of 97.3%/73.3% and 80.0%/90.1% for AMD and DR, respectively. After adjudication by the second expert examiner sensitivity/specificity increases on patient-level to 98.6%/78.3% and 100.0%/92.3% for AMD and DR, respectively.
conclusionThe RetCAD offers very good sensitivity and specificity compared to manual grading by experts and is in line with that obtained by similar automated grading systems. The RetCAD AI software enables simultaneous grading of both AMD and DR based on the same fundus photos. Its sensitivity may be adjusted according to the desired acceptable sensitivity and specificity. Its simplicity cloud base integration allows cost-effective screening where routine expert evaluation may be limited.
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