Deep Learning-assisted AMD Staging based on OCT and OCT Angiography

2026-06-03

AI Summary

This study addresses the challenge of automated, precise staging diagnosis of age-related macular degeneration (AMD) by systematically comparing the performance of deep learning models based on three distinct input modalities: biomarker maps, 2D en face projections, and 3D OCT/OCTA volumetric data. Utilizing an EfficientNet architecture combined with normalization, data augmentation, and five-fold cross-validation, the study evaluates these inputs on a four-stage AMD classification task. Results demonstrate strong agreement between all models and expert annotations (QWK ≥ 0.83), with the biomarker-based model achieving the highest overall and most balanced performance (QWK = 0.85 ± 0.03) and an F1-score of 0.59 ± 0.14 for early AMD detection. The 2D model excels in identifying non-AMD cases with the highest precision (0.79 ± 0.06). This work provides critical guidance for selecting input strategies in automated AMD staging.

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.

Problem

• age-related macular degeneration
• OCT
• OCT angiography
• automated staging
• AMD severity

Innovation

• biomarker-based deep learning
• OCT angiography
• automated AMD staging
• EfficientNet architecture
• early AMD detection