Observational studyActa diabetologica2023
Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting.
Observational study in Acta diabetologica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- Artificial intelligence versus manual screening for the detection of diabetic retinopathy: a comparative systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Trends and hotspots in the field of diabetic retinopathy imaging research from 2000-2023.Frontiers in medicine · 2024Pooled it
- Validation of the Eyerobo FC Portable Fundus Camera for Diabetic Retinopathy Screening Using Public Datasets and Deep Learning.Ophthalmology and therapy · 2026Article
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- Automated Diabetic Retinopathy Screening in Out-patient Diabetes Care - Comparison of Two Artificial Intelligence Algorithms: RetCAD and OphtAI.Klinische Monatsblatter fur Augenheilkunde · 2025Article
- Ophthalmology inpatient consultations: an Irish tertiary hospital experience.Eye (London, England) · 2024Article
- Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.Annals of medicine · 2024Article
- Heatmap analysis for artificial intelligence explainability in diabetic retinopathy detection: illuminating the rationale of deep learning decisions.Annals of translational medicine · 2024Article
- Modern Approach to Diabetic Retinopathy Diagnostics.Diagnostics (Basel, Switzerland) · 2024Review
- Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.Eye and vision (London, England) · 2024Review
- Clinical use cases in artificial intelligence: current trends and future opportunities.Singapore medical journal · 2024Article
- Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol.Ophthalmology scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimDiabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting.
methodsIt was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared.
resultsThe agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891-0.979).
conclusionsThe Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.