Deep Learning-assisted AMD Staging based on OCT and OCT Angiography
Quick Answer
This paper shows that Deep learning models were developed to automate AMD staging using OCT and OCTA data, achieving strong performance with QWK scores above 0.83.
Quick Take
Deep learning models were developed to automate AMD staging using OCT and OCTA data, achieving strong performance with QWK scores above 0.83. The biomarker-based model excelled with QWK = 0.85 and F1-score = 0.59 for early AMD detection, impacting 271 participants aged 50 and older.
Key Points
- 271 participants aged 50+ were analyzed for AMD severity using OCT/OCTA.
- Three deep learning models utilized biomarker maps, 2D, and 3D OCT/OCTA data.
- The biomarker-based model achieved the highest QWK score of 0.85.
- 2D OCT/OCTA model showed the highest precision in identifying eyes without AMD.
- All models demonstrated strong agreement with reference standards in AMD staging.
Article Content
From source RSS / original summaryarXiv: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.
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