ArticleInternational journal of ophthalmology2022
A new handheld fundus camera combined with visual artificial intelligence facilitates diabetic retinopathy screening.
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 18 papers.
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Who cites it
18 citing papers in PubMed, 24 citations in OpenAlex.
- Quantitative Comparison of a Handheld and a Table-Top Fundus Camera for Retinal Microvascular Assessment.Reports (MDPI) · 2026Article
- Evaluation of a New Non-Mydriatic Handheld Fundus Camera for Fundus Imaging in Cats: A Retrospective Study: 208 Cases (2023-2024).Veterinary medicine and science · 2025Article
- Automated detection of diabetic retinopathy lesions in ultra-widefield fundus images using an attention-augmented YOLOv8 framework.Frontiers in cell and developmental biology · 2025Article
- Heatmap analysis for artificial intelligence explainability in diabetic retinopathy detection: illuminating the rationale of deep learning decisions.Annals of translational medicine · 2024Article
- Investigation of the Reproducibility of Portable Optical Coherence Tomography in Diabetic Macular Edema.Pharmaceuticals (Basel, Switzerland) · 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 Implementation of Autonomous Artificial Intelligence Systems for Diabetic Eye Exams: Considerations for Success.Clinical diabetes : a publication of the American Diabetes Association · 2024Article
- Bridging the Camera Domain Gap With Image-to-Image Translation Improves Glaucoma Diagnosis.Translational vision science & technology · 2023Article
- Comparison of 50° handheld fundus camera versus ultra-widefield table-top fundus camera for diabetic retinopathy detection and grading.Eye (London, England) · 2023Article
- Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting.Acta diabetologica · 2023Observational
- Diabetic retinopathy in African-Americans with end-stage kidney disease: a cross-sectional study on prevalence and impact on quality of life.BMJ open diabetes research & care · 2023Article
- Article
- Vault predicting after implantable collamer lens implantation using random forest network based on different features in ultrasound biomicroscopy images.International journal of ophthalmology · 2023Article
- Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023).International journal of ophthalmology · 2023Article
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Automated measurement of the disc-fovea angle based on DeepLabv3.Frontiers in neurology · 2022Article
- Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
10 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo explore the performance in diabetic retinopathy (DR) screening of artificial intelligence (AI) system by evaluating the image quality of a handheld Optomed Aurora fundus camera in comparison to traditional tabletop fundus cameras and the diagnostic accuracy of DR of the two modalities.
methodsOverall, 630 eyes were included from three centers and screened by a handheld camera (Aurora, Optomed, Oulu, Finland) and a table-top camera. Image quality was graded by three masked and experienced ophthalmologists. The diagnostic accuracy of the handheld camera and AI system was evaluated in assessing DR lesions and referable DR.
resultsUnder nonmydriasis status, the handheld fundus camera had better image quality in centration, clarity, and visible range (1.47, 1.48, and 1.40) than conventional tabletop cameras (1.30, 1.28, and 1.18;
conclusionThe handheld Aurora fundus camera combined with autonomous AI system is well-suited in DR screening without mydriasis because of its high sensitivity of DR detection as well as its image quality, but its specificity needs to be improved with better modeling of the data. Use of this new system is safe and effective in the detection of referable DR in real world practice.
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