ArticleTranslational vision science & technology2020
Automatic Identification of Referral-Warranted Diabetic Retinopathy Using Deep Learning on Mobile Phone Images.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.Biomedical engineering online · 2025Pooled it
- Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine-A Systematic Review.Journal of digital imaging · 2023Pooled it
- Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.Translational vision science & technology · 2026Article
- Artificial Intelligence in Medical Education: a Scoping Review of the Evidence for Efficacy and Future Directions.Medical science educator · 2025Review
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- Scientific Production Dynamics in mHealth for Diabetes: Scientometric Analysis.JMIR diabetes · 2024Article
- Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.Eye and vision (London, England) · 2024Review
- Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.The British journal of ophthalmology · 2023Article
- Artificial intelligence to estimate the tear film breakup time and diagnose dry eye disease.Scientific reports · 2023Article
- Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features.PloS one · 2023Article
- Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy.Health information science and systems · 2022Article
- Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models.Diagnostics (Basel, Switzerland) · 2022Article
- [Smartphone-based fundus imaging: applications and adapters].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2022Review
- To pretrain or not? A systematic analysis of the benefits of pretraining in diabetic retinopathy.PloS one · 2022Article
- The Ethical and Societal Considerations for the Rise of Artificial Intelligence and Big Data in Ophthalmology.Frontiers in medicine · 2022Article
- Effect of optical coherence tomography and angiography sampling rate towards diabetic retinopathy severity classification.Biomedical optics express · 2021Article
- Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning.Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society · 2021Article
- Artificial intelligence and ophthalmic surgery.Current opinion in ophthalmology · 2021Review
- Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey.Journal of imaging · 2021Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Purpose: To evaluate the performance of a deep learning algorithm in the detection of referral-warranted diabetic retinopathy (RDR) on low-resolution fundus images acquired with a smartphone and indirect ophthalmoscope lens adapter. Methods: An automated deep learning algorithm trained on 92,364 traditional fundus camera images was tested on a dataset of smartphone fundus images from 103 eyes acquired from two previously published studies. Images were extracted from live video screenshots from fundus examinations using a commercially available lens adapter and exported as a screenshot from live video clips filmed at 1080p resolution. Each image was graded twice by a board-certified ophthalmologist and compared to the output of the algorithm, which classified each image as having RDR (moderate nonproliferative DR or worse) or no RDR. Results: In spite of the presence of multiple artifacts (lens glare, lens particulates/smudging, user hands over the objective lens) and low-resolution images achieved by users of various levels of medical training, the algorithm achieved a 0.89 (95% confidence interval [CI] 0.83-0.95) area under the curve with an 89% sensitivity (95% CI 81%-100%) and 83% specificity (95% CI 77%-89%) for detecting RDR on mobile phone acquired fundus photos. Conclusions: The fully data-driven artificial intelligence-based grading algorithm herein can be used to screen fundus photos taken from mobile devices and identify with high reliability which cases should be referred to an ophthalmologist for further evaluation and treatment. Translational Relevance: The implementation of this algorithm on a global basis could drastically reduce the rate of vision loss attributed to DR.
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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.