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

Researchers developed and evaluated deep learning models for automated age-related macular degeneration (AMD) staging using OCT and OCT angiography data from 271 participants. A biomarker-based model achieved the highest overall performance (QWK = 0.85) and best detection of early AMD, while all models demonstrated strong agreement with the reference standard. The findings show that deep learning can accurately and automatically grade AMD severity, with the biomarker-based approach offering particular value for early detection.

arXiv:2606.05379v1 Announce Type: new 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.