ArticleTranslational vision science & technology2022
A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- An Updated Simplified Severity Scale for Age-Related Macular Degeneration Incorporating Reticular Pseudodrusen: Age-Related Eye Disease Study Report Number 42.Ophthalmology · 2024Trial
- Deep Learning-Based Detection of Reticular Pseudodrusen in Age-Related Macular Degeneration.Clinical & experimental ophthalmologyTrial
- Reticular pseudodrusen load is associated with an increased risk of stroke.BMJ open ophthalmology · 2026Article
- Drusen volume and reticular pseudodrusen volume from optical coherence tomography with deep learning as risk factors for progression to late age-related macular degeneration in eyes with reticular pseudodrusen and contralateral macular neovascularisation.The British journal of ophthalmology · 2026Article
- Optical Coherence Tomography Biomarkers Predicting Progression to Atrophy in Non-Exudative Age-Related Macular Degeneration.Diagnostics (Basel, Switzerland) · 2026Review
- ReticularNet: Automated Pixel-Level Segmentation of Reticular Pseudodrusen on Near-Infrared Reflectance Images by Deep Learning.Ophthalmology science · 2026Article
- Attention-Based Multimodal Deep Learning for Uveal Melanoma Classification Using Ultra-Widefield Fundus Images and Ocular Ultrasound.Ophthalmology science · 2026Article
- Determinants of Intergrader Agreement for Key Retinal Photography and OCT Biomarkers in AMD.Translational vision science & technology · 2025Article
- Genetic Associations of Rod- and Cone-Mediated Vision in Aging and Age-Related Macular Degeneration.Investigative ophthalmology & visual science · 2025Article
- Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.Scientific reports · 2025Article
- Assessing Reliability and Agreement in Topographic Measurement of Reticular Pseudodrusen Area: A Multimodal Imaging Approach.Retina (Philadelphia, Pa.) · 2025Article
- Artificial intelligence in managing retinal disease-current concepts and relevant aspects for health care providers.Wiener medizinische Wochenschrift (1946) · 2025Review
- Artificial intelligence in assessing progression of age-related macular degeneration.Eye (London, England) · 2025Review
- Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration.Scientific reports · 2024Article
- Retinal Pigment Epithelium Curvature Can Predict Late Age-Related Macular Degeneration.Investigative ophthalmology & visual science · 2024Article
- [Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration].Die Ophthalmologie · 2024Review
- Chondrosarcoma evaluation using hematein-based x-ray staining and high-resolution 3D micro-CT: a feasibility study.European radiology experimental · 2024Article
- Artificial intelligence in age-related macular degeneration: state of the art and recent updates.BMC ophthalmology · 2024Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Purpose: The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans. Methods: A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), kappa, accuracy, intraclass correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curves. Results: The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification models). The mean ICC for the drusen and RPD area versus graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. Conclusions: The models achieved high classification and segmentation performance, similar to human performance. Translational Relevance: Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings.
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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.