Deep Learning-assisted AMD Staging based on OCT and OCT Angiography
This study develops deep learning models for automated staging of age-related macular degeneration (AMD) using OCT/OCTA data. Among 271 participants, three models were tested: biomarker-based, 2D en face projections, and 3D volumes. All models showed strong performance, with the biomarker-based model achieving the best overall results (QWK=0.85) and particular strength in early AMD detection.
[2606.05379] Deep Learning-assisted AMD Staging based on OCT and OCT Angiography
[Submitted on 3 Jun 2026]
Title:Deep Learning-assisted AMD Staging based on OCT and OCT Angiography
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Abstract:To develop and evaluate deep learning models for automated grading of age-related macular degeneration (AMD) severity using optical coherence tomography (OCT) and OCT angiography (OCTA) data. Two hundred seventy-one participants aged >= 50 years with varying AMD severities. Central macular 6 x 6 mm OCT/OCTA volumes were acquired using a swept-source OCTA system (SOLIX; Visionix/Optovue Inc., CA). AMD severity was graded into four stages (No AMD, Early AMD, Intermediate AMD, and Advanced AMD) according to the AREDS simplified severity scale. Three deep learning models were developed using different input modalities: (1) biomarker maps derived from segmented pathological features, including retinal fluid, drusen, geographic atrophy (GA), and macular neovascularization (MNV); (2) two-dimensional (2D) en face OCT and OCTA projections; and (3) three-dimensional (3D) OCT/OCTA volumes. EfficientNet-based architectures were trained using normalized inputs, data augmentation, and five-fold cross-validation. A total of 2,030 OCT/OCTA volumes from 351 eyes of 271 participants were analyzed. All models demonstrated strong AMD staging performance with substantial agreement with the reference standard (QWK >= 0.83). The biomarker-based model achieved the highest overall performance (QWK = 0.85 +/- 0.03, mean +/- standard deviation) and the best detection of early AMD (F1-score = 0.59 +/- 0.14). The 3D model achieved performance comparable to the 2D OCT/OCTA model (QWK = 0.83 +/- 0.04 vs. 0.83 +/- 0.09), while the 2D OCT/OCTA model showed the highest precision (0.79 +/- 0.06) and most accurately identified eyes without AMD. Deep learning models using OCT/OCTA data can accurately and automatically grade AMD severity. Among the evaluated approaches, the biomarker-based model provided the most balanced performance and showed particular value for early AMD detection.
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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.05379 [cs.CV]
(or arXiv:2606.05379v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.05379
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yukun Guo [view email] [v1] Wed, 3 Jun 2026 19:28:35 UTC (968 KB)
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