ArticleJournal of diabetes science and technology2022
Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera.
Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it, 97 citations in OpenAlex.
- Effectiveness of screening modalities for early detection of diabetic retinopathy: a systematic review and meta-analysis of tele-ophthalmology, AI-based tools, and conventional methods.Frontiers in medicine · 2026Pooled it
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- A comprehensive review on automated diabetic retinopathy detection and classification using fundus image.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Accuracy of portable artificial intelligence-supported fundus camera in the screening of diabetic retinopathy in primary care.Archives of endocrinology and metabolism · 2026Article
- Diabetic Retinopathy in Primary Healthcare in a Brazilian Municipality: A Cross-Sectional Study Using Teleophthalmology.Health care science · 2026Article
- Evaluating ChatGPT-5 for Detection of Barrett's Esophagus and Grading of Esophagitis: A Multiclass Endoscopic Image Analysis.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Smartphone-based lightweight AI system for real-time multiple anterior segment disease screening: development and real-world validation.BMC medicine · 2026Article
- Real-World Performance of Artificial Intelligence in Diabetic Retinopathy Screening: A Systematic Review.Cureus · 2026Review
- Implementation of diabetic retinopathy screening in the Oslo region, Norway: a 3-year pilot study.Frontiers in medicine · 2026Article
- Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes.NPJ digital medicine · 2025Article
- Diagnostic and Screening AI Tools in Brazil's Resource-Limited Settings: Systematic Review.JMIR AI · 2025Review
- Integrating Nursing-Teleophthalmology Improves Diabetic Retinopathy Screening in Primary Healthcare, Reducing Unnecessary Referrals to Specialist Healthcare.International journal of nursing practice · 2025Article
- Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study.Journal of diabetes science and technology · 2025Article
- A portable retina fundus photos dataset for clinical, demographic, and diabetic retinopathy prediction.Scientific data · 2025Article
- Artificial intelligence in proliferative diabetic retinopathy: advancing diagnosis, precision surgery, and anti-VEGF therapy optimization.Frontiers in medicine · 2025Review
- Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.Annals of medicine · 2024Article
- Utility of handheld non-mydriatic fundoscopy in a case of bilateral, reversible vision loss in an advanced HIV patient with cryptococcal meningitis in Sub-Saharan Africa.Medical mycology case reports · 2024Article
- Diagnostic accuracy of a smartphone-based device (VistaView) for detection of diabetic retinopathy: A prospective study.PLOS digital health · 2024Article
- Heatmap analysis for artificial intelligence explainability in diabetic retinopathy detection: illuminating the rationale of deep learning decisions.Annals of translational medicine · 2024Article
- Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population.Diabetology & metabolic syndrome · 2024Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPortable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting.
methodParticipants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR.
resultsA total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 0·89. All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading.
conclusionsThe DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs.
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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.