ArticleOphthalmology2020
Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2.
Article in Ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 43 citations in OpenAlex.
- 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
- ReticularNet: Automated Pixel-Level Segmentation of Reticular Pseudodrusen on Near-Infrared Reflectance Images by Deep Learning.Ophthalmology science · 2026Article
- A Datasheet for Age-Related Eye Disease Study 2 on the Database of Genotypes and Phenotypes.Ophthalmology science · 2026Article
- Deep Learning for Comprehensive Analysis of Retinal Fundus Images: Detection of Systemic and Ocular Conditions.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- [Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration].Die Ophthalmologie · 2024Review
- Intergrader Agreement in Grading Optical Coherence Tomography Morphologic Features in Eyes With Intermediate Nonexudative Age-Related Macular Degeneration.Translational vision science & technology · 2024Article
- Interpretable multimodal classification for age-related macular degeneration diagnosis.PloS one · 2024Article
- Deep-GA-Net for Accurate and Explainable Detection of Geographic Atrophy on OCT Scans.Ophthalmology science · 2023Article
- Reticular Pseudodrusen Status, ARMS2/HTRA1 Genotype, and Geographic Atrophy Enlargement: Age-Related Eye Disease Study 2 Report 32.Ophthalmology · 2023Article
- Reticular Pseudodrusen: The Third Macular Risk Feature for Progression to Late Age-Related Macular Degeneration: Age-Related Eye Disease Study 2 Report 30.Ophthalmology · 2022Article
- Automated diagnosing primary open-angle glaucoma from fundus image by simulating human's grading with deep learning.Scientific reports · 2022Article
- DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.Ophthalmology · 2022Article
- Cluster Analysis and Genotype-Phenotype Assessment of Geographic Atrophy in Age-Related Macular Degeneration: Age-Related Eye Disease Study 2 Report 25.Ophthalmology. Retina · 2021Article
- Improving Interpretability in Machine Diagnosis: Detection of Geographic Atrophy in OCT Scans.Ophthalmology science · 2021Article
- Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Subretinal drusenoid deposits: An update.Taiwan journal of ophthalmologyReview
- A deep learning model for generating fundus autofluorescence images from color fundus photography.Advances in ophthalmology practice and researchArticle
- Deep-Reticular Pseudodrusen-Net: A 3-Dimensional Deep Network for Detection of Reticular Pseudodrusen on OCT Scans.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo develop deep learning models for detecting reticular pseudodrusen (RPD) using fundus autofluorescence (FAF) images or, alternatively, color fundus photographs (CFP) in the context of age-related macular degeneration (AMD).
designApplication of deep learning models to the Age-Related Eye Disease Study 2 (AREDS2) dataset.
participantsFAF and CFP images (n = 11 535) from 2450 AREDS2 participants. Gold standard labels from reading center grading of the FAF images were transferred to the corresponding CFP images.
methodsA deep learning model was trained to detect RPD in eyes with intermediate to late AMD using FAF images (FAF model). Using label transfer from FAF to CFP images, a deep learning model was trained to detect RPD from CFP (CFP model). Performance was compared with 4 ophthalmologists using a random subset from the full test set.
main outcome measuresArea under the receiver operating characteristic curve (AUC), κ value, accuracy, and F1 score.
resultsThe FAF model had an AUC of 0.939 (95% confidence interval [CI], 0.927-0.950), a κ value of 0.718 (95% CI, 0.685-0.751), and accuracy of 0.899 (95% CI, 0.887-0.911). The CFP model showed equivalent values of 0.832 (95% CI, 0.812-0.851), 0.470 (95% CI, 0.426-0.511), and 0.809 (95% CI, 0.793-0.825), respectively. The FAF model demonstrated superior performance to 4 ophthalmologists, showing a higher κ value of 0.789 (95% CI, 0.675-0.875) versus a range of 0.367 to 0.756 and higher accuracy of 0.937 (95% CI, 0.907-0.963) versus a range of 0.696 to 0.933. The CFP model demonstrated substantially superior performance to 4 ophthalmologists, showing a higher κ value of 0.471 (95% CI, 0.330-0.606) versus a range of 0.105 to 0.180 and higher accuracy of 0.844 (95% CI, 0.798-0.886) versus a range of 0.717 to 0.814.
conclusionsDeep learning-enabled automated detection of RPD presence from FAF images achieved a high level of accuracy, equal or superior to that of ophthalmologists. Automated RPD detection using CFP achieved a lower accuracy that still surpassed that of ophthalmologists. Deep learning models can assist, and even augment, the detection of this clinically important AMD-associated lesion.
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