ReviewJournal of current ophthalmology
The Utility of Smartphone-Based Artificial Intelligence Approaches for Diabetic Retinopathy: A Literature Review and Meta-Analysis.
Review in Journal of current ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Describing the Performance and the Infrastructure Requirements of the Existing Artificial Intelligence (AI)-Based Diabetic Retinopathy (DR) Screening Algorithms for Diabetic Patients: an Umbrella Review.Journal of medical systems · 2025Pooled it
- Application of smartphone ophthalmoscope in ophthalmic clinical practice and teaching.International journal of ophthalmology · 2025Review
- Integrated image-based deep learning and language models for primary diabetes care.Nature medicine · 2024Article
- Clinical utility of handheld fundus and smartphone-based camera for monitoring diabetic retinal diseases: a review study.International ophthalmology · 2024Review
- Development and validation of a risk prediction model for diabetic retinopathy in type 2 diabetic patients.Scientific reports · 2023Article
- Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis.PLOS global public health · 2023Article
- Review
- The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques.Frontiers in medicine · 2022Review
- Smartphone-enabled retinal arteriovenous imaging and correlation with coronary SYNTAX score.Indian heart journalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo assess the diagnostic accuracy measures such as sensitivity and specificity of smartphone-based artificial intelligence (AI) approaches in the detection of diabetic retinopathy (DR).
methodsA literature search of the EMBASE and MEDLINE databases (up to March 2020) was conducted. Only studies using both smartphone-based cameras and AI software for image analysis were included. The main outcome measures were pooled sensitivity and specificity, diagnostic odds ratios and relative risk of smartphone-based AI approaches in detecting DR (of all types), and referable DR (RDR) (moderate nonproliferative retinopathy or worse and/or the presence of diabetic macular edema).
resultsSmartphone-based AI has a pooled sensitivity of 89.5% (95% confidence interval [CI]: 82.3%-94.0%) and pooled specificity of 92.4% (95% CI: 86.4%-95.9%) in detecting DR. For referable disease, sensitivity is 97.9% (95% CI: 92.6%-99.4%), and the pooled specificity is 85.9% (95% CI: 76.5%-91.9%). The technology is better at correctly identifying referable retinopathy.
conclusionsThe smartphone-based AI programs demonstrate high diagnostic accuracy for the detection of DR and RDR and are potentially viable substitutes for conventional diabetic screening approaches. Further, high-quality randomized controlled trials are required to establish the effectiveness of this approach in different populations.
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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.