Trial reportNature communications2020
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning.
Trial report in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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
53 citing papers in PubMed, 131 citations in OpenAlex.
- Article
- Co-Evaporated Ratio-Tunable TeSmall (Weinheim an der Bergstrasse, Germany) · 2026Article
- Deep Learning Prediction of Retinal Thickness from Near-Infrared Fundus Photography: Toward Decentralized Quantitative Assessment of Diabetic Macular Edema.Journal of personalized medicine · 2026Article
- Prospective Data Curation Enables High-Performance Artificial Intelligence for Diabetic Retinopathy Screening in a Resource-Limited Setting.Ophthalmology science · 2026Article
- Volumetric montaging of optical coherence tomography in human retinas.bioRxiv : the preprint server for biology · 2026Article
- The Central Role of Oxidative Stress in Diabetic Retinopathy: Advances in Pathogenesis, Diagnosis, and Therapy.Diagnostics (Basel, Switzerland) · 2026Review
- AI-based prediction of best-corrected visual acuity in patients with multiple retinal diseases using multimodal medical imaging.The British journal of ophthalmology · 2026Article
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Artificial Intelligence in Ocular Oncology: Differentiating Choroidal Melanocytic Lesions.Ophthalmology science · 2026Article
- Artificial Intelligence in Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
- Robotic Optical Coherence Tomography With Expanded Three-Dimensional Field-of-View Using Point-Cloud-Based Volumetric Montaging.IEEE transactions on medical imaging · 2025Article
- Detection of Center-Involved Diabetic Macular Edema With Visual Impairment Using Multimodal Artificial Intelligence Algorithms.Ophthalmology. Retina · 2025Article
- How early can we detect diabetic retinopathy? A narrative review of imaging tools for structural assessment of the retina.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- Investigation of the Reproducibility of Portable Optical Coherence Tomography in Diabetic Macular Edema.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN.Scientific reports · 2024Article
- Cost-effectiveness of incorporating self-imaging optical coherence tomography into fundus photography-based diabetic retinopathy screening.NPJ digital medicine · 2024Article
- Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.Eye and vision (London, England) · 2024Review
- Fairer AI in ophthalmology via implicit fairness learning for mitigating sexism and ageism.Nature communications · 2024Article
- Rate and Predictors of Misclassification of Active Diabetic Macular Edema as Detected by an Automated Retinal Image Analysis System.Ophthalmology and therapy · 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
18 authors at 4 institutions in 3 countries.
Funding
Abstract
Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p < 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81-0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85-0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging.
Indexed as
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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.