ArticleAnnals of medicine2024
Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- 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
- Diagnostic performance of an artificial intelligence software for diabetic retinopathy organised screening in a pilot study.Scientific reports · 2026Article
- Clinical setting-dependent diagnostic accuracy of artificial intelligence and store-and-forward diabetic retinopathy screening: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Quantitative Comparison of a Handheld and a Table-Top Fundus Camera for Retinal Microvascular Assessment.Reports (MDPI) · 2026Article
- Validation of the Eyerobo FC Portable Fundus Camera for Diabetic Retinopathy Screening Using Public Datasets and Deep Learning.Ophthalmology and therapy · 2026Article
- Automated Diabetic Retinopathy Screening in Out-patient Diabetes Care - Comparison of Two Artificial Intelligence Algorithms: RetCAD and OphtAI.Klinische Monatsblatter fur Augenheilkunde · 2025Article
- Diabetic retinal disease.Nature reviews. Disease primers · 2025Review
- Performance of a Retinal Imaging Camera With On-Device Intelligence for Primary Care: Retrospective Study.JMIR formative research · 2025Article
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- The evolution of diabetic retinopathy screening.Eye (London, England) · 2025Review
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- 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
- Analysis of risk factors for painful diabetic peripheral neuropathy and construction of a prediction model based on Lasso regression.Frontiers in endocrinology · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with diabetes is increasing and telemedicine, mobile handheld devices and automated solutions may alleviate the burden for healthcare. We compared the performance of 21 artificial intelligence (AI) algorithms for referable DR screening in datasets taken by handheld Optomed Aurora fundus camera in a real-world setting. PATIENTS AND
methodsProspective study of 156 patients (312 eyes) attending DR screening and follow-up. Both papilla- and macula-centred 50° fundus images were taken from each eye. DR was graded by experienced ophthalmologists and 21 AI algorithms.
resultsMost eyes, 183 out of 312 (58.7%), had no DR and mild NPDR was noted in 21 (6.7%) of the eyes. Moderate NPDR was detected in 66 (21.2%) of the eyes, severe NPDR in 1 (0.3%), and PDR in 41 (13.1%) composing a group of 34.6% of eyes with referable DR. The AI algorithms achieved a mean agreement of 79.4% for referable DR, but the results varied from 49.4% to 92.3%. The mean sensitivity for referable DR was 77.5% (95% CI 69.1-85.8) and specificity 80.6% (95% CI 72.1-89.2). The rate for images ungradable by AI varied from 0% to 28.2% (mean 1.9%). Nineteen out of 21 (90.5%) AI algorithms resulted in grading for DR at least in 98% of the images.
conclusionsFundus images captured with Optomed Aurora were suitable for DR screening. The performance of the AI algorithms varied considerably emphasizing the need for external validation of screening algorithms in real-world settings before their clinical application.
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