ArticleNPJ digital medicine2019
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 88 papers, 8 of them syntheses that pooled it.
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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
88 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- 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
- 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
- Trends and hotspots in the field of diabetic retinopathy imaging research from 2000-2023.Frontiers in medicine · 2024Pooled it
- Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review.Journal of medical screening · 2023Pooled it
- A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images.Translational vision science & technology · 2023Pooled it
- Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta-analysis.Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery · 2022Pooled it
- Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.Journal of medical Internet research · 2021Pooled it
- Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis.PloS one · 2021Pooled it
- Uncertainty-Aware AI-Assisted Diabetic Retinopathy Grading from Fundus Images with Ordinal Conformal Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- REAL-WORLD PRACTICE OF ARTIFICIAL INTELLIGENCE DIAGNOSTIC SYSTEM FOR DIABETIC RETINOPATHY IN TAIWAN.Retina (Philadelphia, Pa.) · 2026Article
- A Roadmap for National Diabetic Retinopathy Screening in Croatia: Integrating European Evidence, Telemedicine, and AI.Medicina (Kaunas, Lithuania) · 2026Review
- Cross-cultural adaptation and psychometric evaluation of the Shinners Artificial Intelligence Perception Scale (SAIP): evidence from Turkish nursing students.BMC nursing · 2026Article
- Artificial intelligence-based algorithm for predicting outcomes in early-stage lung cancer: An annotation-free imaging artificial intelligence study.JTCVS open · 2026Article
- BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction.Scientific reports · 2026Article
- Closing the loop: human-augmented, mechanistically enhanced AI for proactive management of drug-drug interactions.Frontiers in pharmacology · 2026Article
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- Artificial Intelligence in Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- A Deep Learning Segmentation Model for Detection of Active Proliferative Diabetic Retinopathy.Ophthalmology and therapy · 2025Article
- CT-based artificial intelligence system complementing deep learning model and radiologist for liver fibrosis staging.iScience · 2025Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
32 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy. This study aims to validate one such algorithm on a large-scale clinical population, and compare the algorithm performance with that of human graders. A total of 25,326 gradable retinal images of patients with diabetes from the community-based, nationwide screening program of DR in Thailand were analyzed for DR severity and referable diabetic macular edema (DME). Grades adjudicated by a panel of international retinal specialists served as the reference standard. Relative to human graders, for detecting referable DR (moderate NPDR or worse), the deep learning algorithm had significantly higher sensitivity (0.97 vs. 0.74,
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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.