ArticleBMJ open diabetes research & care2020
Simple, Mobile-based Artificial Intelligence Algo
Article in BMJ open diabetes research & care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
37 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Diabetic Retinopathy Screening Approaches in Developing Countries: A Systematic Review and Meta-Analysis.Turkish journal of ophthalmology · 2025Pooled it
- 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
- Machine Learning and Smart Devices for Diabetes Management: Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.Journal of medical Internet research · 2021Pooled it
- Trial
- Detection of Posterior Segment Eye Disease in Rural Eye Camps in South India: A Nonrandomized Cluster Trial.Ophthalmology. Retina · 2021Trial
- Using artificial intelligence-assisted retinal imaging devices operated by non-specialist workers in detecting diabetic retinopathy in Sri Lanka.BMC global and public health · 2026Article
- Article
- Camera-Agnostic Autonomous Diagnosis of Glaucomatous Optic Neuropathy using Macular Fundus Imaging and Machine Learning.medRxiv : the preprint server for health sciences · 2026Article
- Pediatric diabetes prediction using machine learning.Scientific reports · 2026Article
- Real-world validation of an AI-based non-mydriatic fundus camera in the diagnosis of diabetic retinopathy in Liberia.Frontiers in digital health · 2026Article
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- AI in the Shadows: Unveiling the Strengths and Blind Spots of Medios AI Retinal Screening in Cancer Care.Cureus · 2025Article
- Review
- Advancing AMD screening with an offline, AI-powered smartphone-based fundus camera: A prospective, real-world clinical validation.Eye (London, England) · 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
- Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning.World journal of clinical cases · 2025Article
- Narrative review of comprehensive management strategies for diabetic retinopathy: interdisciplinary approaches and future perspectives.BMJ public health · 2025Review
- A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression.Scientific reports · 2024Article
- Investigation of the Reproducibility of Portable Optical Coherence Tomography in Diabetic Macular Edema.Pharmaceuticals (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images.
methodsThis cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth).
resultsAnalysis included images from 900 individuals (252 had DR). For any DR, the sensitivity and specificity of the AI algorithm was found to be 83.3% (95% CI 80.9% to 85.7%) and 95.5% (95% CI 94.1% to 96.8%). The sensitivity and specificity of the AI algorithm in detecting referable DR (RDR) was 93% (95% CI 91.3% to 94.7%) and 92.5% (95% CI 90.8% to 94.2%).
conclusionThe Medios AI has a high sensitivity and specificity in the detection of RDR using NM retinal images.
Indexed as
Identifiers
What OpenQuestion holds
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.