SynthesisJournal of medical Internet research2020
Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies.
Synthesis in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- Diabetic Retinopathy Screening Approaches in Developing Countries: A Systematic Review and Meta-Analysis.Turkish journal of ophthalmology · 2025Pooled it
- Artificial intelligence versus manual screening for the detection of diabetic retinopathy: a comparative systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Systemic Diseases and the Eye: A Translational Perspective on Diabetes.Chronic diseases and translational medicine · 2026Review
- Review
- Comparative Evaluation of Smartphone-Based Fundus Imaging Systems in Dogs and Cats.Veterinary ophthalmology · 2026Article
- Application of smartphone ophthalmoscope in ophthalmic clinical practice and teaching.International journal of ophthalmology · 2025Review
- Two Handheld Retinograph Devices Evaluated by Ophthalmologists and an Artificial Intelligence Algorithm.Journal of clinical medicine · 2024Article
- Diagnostic accuracy of a smartphone-based device (VistaView) for detection of diabetic retinopathy: A prospective study.PLOS digital health · 2024Article
- Advances in Structural and Functional Retinal Imaging and Biomarkers for Early Detection of Diabetic Retinopathy.Biomedicines · 2024Review
- Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.Eye and vision (London, England) · 2024Review
- Evaluating imaging repeatability of fully self-service fundus photography within a community-based eye disease screening setting.Biomedical engineering online · 2024Article
- Diagnostic Accuracy of Hand-Held Fundus Camera and Artificial Intelligence in Diabetic Retinopathy Screening.Biomedicines · 2023Article
- Impact of COVID-19 pandemic on behavioral changes and glycemic control and a survey of telemedicine in patients with diabetes: A multicenter retrospective observational study.Journal of diabetes investigation · 2023Observational
- URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Development and validation of a risk prediction model for diabetic retinopathy in type 2 diabetic patients.Scientific reports · 2023Article
- Accuracy of Low-Cost, Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening: A Systematic Review.Clinical ophthalmology (Auckland, N.Z.) · 2023Review
- Ophthalmic imaging in diabetic retinopathy: A review.Clinical & experimental ophthalmology · 2022Review
- Detection and Diagnosis of Retinoblastoma: Can Mobile Devices Be the Next Step Toward Early Intervention?Cureus · 2022Review
- Improving Glycemic Control in Type 2 Diabetes Using Mobile Applications and e-Coaching: A Mixed Treatment Comparison Network Meta-Analysis.Journal of diabetes science and technology · 2022Article
- Diabetic retinopathy screening and treatment through the Brazilian National Health Insurance.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetic retinopathy (DR), a common complication of diabetes mellitus, is the leading cause of impaired vision in adults worldwide. Smartphone ophthalmoscopy involves using a smartphone camera for digital retinal imaging. Utilizing smartphones to detect DR is potentially more affordable, accessible, and easier to use than conventional methods.
objectiveThis study aimed to determine the diagnostic accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients.
methodsWe performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for literature published from January 2000 to November 2018. We included studies involving diabetic patients, which compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting DR to an accurate or commonly employed reference standard, such as indirect ophthalmoscopy, slit-lamp biomicroscopy, and tabletop fundus photography. Two reviewers independently screened studies against the inclusion criteria, extracted data, and assessed the quality of included studies using the Quality Assessment of Diagnostic Accuracy Studies-2 tool, with disagreements resolved via consensus. Sensitivity and specificity were pooled using the random effects model. A summary receiver operating characteristic (SROC) curve was constructed. This review is reported in line with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines.
resultsIn all, nine studies involving 1430 participants were included. Most studies were of high quality, except one study with limited applicability because of its reference standard. The pooled sensitivity and specificity for detecting any DR was 87% (95% CI 74%-94%) and 94% (95% CI 81%-98%); mild nonproliferative DR (NPDR) was 39% (95% CI 10%-79%) and 95% (95% CI 91%-98%); moderate NPDR was 71% (95% CI 57%-81%) and 95% (95% CI 88%-98%); severe NPDR was 80% (95% CI 49%-94%) and 97% (95% CI 88%-99%); proliferative DR (PDR) was 92% (95% CI 79%-97%) and 99% (95% CI 96%-99%); diabetic macular edema was 79% (95% CI 63%-89%) and 93% (95% CI 82%-97%); and referral-warranted DR was 91% (95% CI 86%-94%) and 89% (95% CI 56%-98%). The area under SROC curve ranged from 0.879 to 0.979. The diagnostic odds ratio ranged from 11.3 to 1225.
conclusionsWe found heterogeneous evidence showing that smartphone ophthalmoscopy performs well in detecting DR. The diagnostic accuracy for PDR was highest. Future studies should standardize reference criteria and classification criteria and evaluate other available forms of smartphone ophthalmoscopy in primary care settings.
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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.