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arXiv · 2601.18826

OCTA-Based Biomarker Characterization in nAMD

MAria Simona Tivadar·Ioana Damian·Adrian Groza·Simona Delia Nicoara
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Abstract

We aim to enhance ophthalmologists' decision-making when diagnosing the Neovascular Age-Related Macular Degeneration (nAMD). We developed three tools to analyze Optical Coherence Tomography Angiography images: (1) extracting biomarkers such as mCNV area and vessel density using image processing; (2) generating a 3D visualization of the neovascularization for a better view of the affected regions; and (3) applying an ensemble of three white box machine learning algorithms (decision tree, support vector machines and DL-Learner) for nAMD diagnosis. The learned expressions reached 100% accuracy for the training data and 68% accuracy in testing. The main advantage is that all the learned models white-box, which ensures explainability and transparency, allowing clinicians to better understand the decision-making process.

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MAria Simona Tivadar, Ioana Damian, Adrian Groza, Simona Delia Nicoara. 2026-01-25. OCTA-Based Biomarker Characterization in nAMD. https://doi.org/10.1109/ipas63548.2025.10924557

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