OCTA-Based Biomarker Characterization in nAMD

TL;DR

This paper introduces OCTA-based tools for analyzing biomarkers, visualizing neovascularization in 3D, and applying explainable machine learning models to improve diagnosis of nAMD, with a focus on transparency and clinical utility.

Contribution

It presents a novel combination of image processing, 3D visualization, and explainable machine learning for nAMD diagnosis using OCTA images.

Findings

Achieved 100% accuracy on training data

Achieved 68% accuracy on testing data

Provided explainable models for clinical decision-making

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.