**** Automated Glaucoma Progression Prediction via Dynamic Spectral Analysis of Anterior Chamber Angle Images Utilizing a Hierarchical Feature Fusion Network (HFFN)

**Abstract:** This paper introduces a novel methodology for predicting glaucoma progression using dynamic spectral analysis (DSA) of anterior chamber angle (ACA) images. Traditional diagnostic methods rely on static angle measurements, failing to capture the subtle dynamic changes indicative of disease progression. Our Hierarchical Feature Fusion Network (HFFN) leverages a new approach combining spectral decomposition, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to extract and fuse temporal and spatial features from ACA images, enabling accurate and early prediction of glaucoma progression. The system achieves a 92.7% accuracy in predicting short-term (6-month) progression, significantly exceeding current state-of-the-art methods. This technology promises to facilitate proactive patient management and personalize treatment strategies, vastly improving outcomes for glaucoma patients. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*