ArticleOphthalmology2014
Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography.
Article in Ophthalmology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00734487 (Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study), which is not on this map. Cited by 130 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.
Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study
Who cites it
130 citing papers in PubMed, 2 syntheses or guidelines pooled it, 369 citations in OpenAlex.
- Biomarkers for Nonexudative Age-Related Macular Degeneration and Relevance for Clinical Trials: A Systematic Review.Molecular diagnosis & therapy · 2021Pooled it
- Genome-wide meta-analysis identifies novel loci associated with age-related macular degeneration.Journal of human genetics · 2020Pooled it
- Localized Optical Coherence Tomography Precursors of Macular Atrophy and Fibrotic Scar in the Comparison of Age-Related Macular Degeneration Treatments Trials.American journal of 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
- Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation.Scientific reports · 2026Article
- Shifting the retinal foundation models paradigm from slices to volumes for optical coherence tomography.NPJ digital medicine · 2026Article
- Comprehensive 3D Optical Coherence Tomography Dataset for AMD and DME: Facilitating Deep-Learning-Based 3D Segmentation.Scientific data · 2026Article
- BanglaOCT2025: A Population-Specific Fovea-Centric OCT Dataset with Self-Supervised Volumetric Restoration Using Flip-Flop Swin Transformers.Diagnostics (Basel, Switzerland) · 2026Article
- Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.Progress in retinal and eye research · 2026Review
- The Clinical Significance of Imaging Biomarkers Discoverable by Anomaly Detection Methods in Retinal Diseases: A Review.Journal of ophthalmology · 2026Review
- Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis.NPJ digital medicine · 2025Article
- Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks.Bioengineering (Basel, Switzerland) · 2025Review
- Publicly available imaging datasets for age-related macular degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reusable (FAIR) principles.Experimental eye research · 2025Review
- OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers.Scientific data · 2025Article
- Bibliometric analysis of research on the application of deep learning to ophthalmology.Quantitative imaging in medicine and surgery · 2025Article
- OculusNet: Detection of retinal diseases using a tailored web-deployed neural network and saliency maps for explainable AI.Frontiers in medicine · 2025Article
- From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification.Journal of medical signals and sensors · 2025Article
- A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer.Diagnostics (Basel, Switzerland) · 2024Article
- Addressing fairness issues in deep learning-based medical image analysis: a systematic review.NPJ digital medicine · 2024Review
70 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
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo define quantitative indicators for the presence of intermediate age-related macular degeneration (AMD) via spectral-domain optical coherence tomography (SD-OCT) imaging of older adults.
designEvaluation of diagnostic test and technology. PARTICIPANTS AND CONTROLS: One eye from 115 elderly subjects without AMD and 269 subjects with intermediate AMD from the Age-Related Eye Disease Study 2 (AREDS2) Ancillary SD-OCT Study.
methodsWe semiautomatically delineated the retinal pigment epithelium (RPE) and RPE drusen complex (RPEDC, the axial distance from the apex of the drusen and RPE layer to Bruch's membrane) and total retina (TR, the axial distance between the inner limiting and Bruch's membranes) boundaries. We registered and averaged the thickness maps from control subjects to generate a map of "normal" non-AMD thickness. We considered RPEDC thicknesses larger or smaller than 3 standard deviations from the mean as abnormal, indicating drusen or geographic atrophy (GA), respectively. We measured TR volumes, RPEDC volumes, and abnormal RPEDC thickening and thinning volumes for each subject. By using different combinations of these 4 disease indicators, we designed 5 automated classifiers for the presence of AMD on the basis of the generalized linear model regression framework. We trained and evaluated the performance of these classifiers using the leave-one-out method.
main outcome measuresThe range and topographic distribution of the RPEDC and TR thicknesses in a 5-mm diameter cylinder centered at the fovea.
resultsThe most efficient method for separating AMD and control eyes required all 4 disease indicators. The area under the curve (AUC) of the receiver operating characteristic (ROC) for this classifier was >0.99. Overall neurosensory retinal thickening in eyes with AMD versus control eyes in our study contrasts with previous smaller studies.
conclusionsWe identified and validated efficient biometrics to distinguish AMD from normal eyes by analyzing the topographic distribution of normal and abnormal RPEDC thicknesses across a large atlas of eyes. We created an online atlas to share the 38 400 SD-OCT images in this study, their corresponding segmentations, and quantitative measurements.
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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.