ArticleBiomedical optics express2017
Deep-learning based, automated segmentation of macular edema in optical coherence tomography.
Article in Biomedical optics express, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05747144 (Multimodal Imaging in Vitreo-retinal Surgery and Macular Dystrophies), which is not on this map. Cited by 154 papers, 5 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.
Multimodal Imaging in Vitreo-retinal Surgery and Macular Dystrophies: Biomarkers of Morpho-Functional Recovery by Artificial Intelligence
Who cites it
154 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.Translational vision science & technology · 2025Pooled it
- Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: a systematic review.Eye (London, England) · 2023Pooled it
- Adherence of randomised controlled trials using artificial intelligence in ophthalmology to CONSORT-AI guidelines: a systematic review and critical appraisal.BMJ health & care informatics · 2023Pooled it
- Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.Current diabetes reports · 2019Pooled it
- Spectral-Domain OCT Measurements in Alzheimer's Disease: A Systematic Review and Meta-analysis.Ophthalmology · 2019Pooled it
- Longitudinal Higher-Order OCT Assessment of Quantitative Fluid Dynamics and the Total Retinal Fluid Index in Neovascular AMD.Translational vision science & technology · 2021Trial
- Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study.Ophthalmology · 2021Trial
- Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.Ophthalmology · 2020Trial
- Self-Supervised OCT Representation Learning with Local Dimensionality Regularization for Automated Retinal Disease Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- Quantitative Analysis of Retinal Fluid by a Deep Learning Model in Uveitic Macular Edema.Ophthalmology science · 2026Article
- Review
- Outer retinal band segmentation in healthy subjects: comparative study between human grading and deep convolutional neural networks.Quantitative imaging in medicine and surgery · 2026Article
- DPLSeg: unsupervised general segmentation model for retinal images across multiple OCT devices.Biomedical optics express · 2026Article
- AI-MK: artificial intelligence for assessing and monitoring microbial keratitis.BMJ open ophthalmology · 2026Observational
- Leveraging CNN and Transfer Learning With EfficientNet for Enhanced Optical Coherence Tomography (OCT).International journal of biomedical imaging · 2026Article
- AMD-Mamba: A Phenotype-Aware Multi-modal Framework for Robust AMD Prognosis.Machine learning in medical imaging. MLMI (Workshop) · 2026Article
- Artificial intelligence in ophthalmology clinical trials: a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.Progress in retinal and eye research · 2026Review
- Artificial Intelligence in Label-Free Optical Imaging Applications.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Development of a deep learning model to classify choroidal melanoma risk factors based on color fundus photographs.AJO international · 2025Article
94 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts. Additionally, the agreement between experts and between experts and CNN were similar. Our results reveal that CNN can be trained to perform automated segmentations of clinically relevant image features.
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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.