Unveiling Glaucoma Progression

Abstract

Glaucoma progression is primarily assessed through functional deterioration, most commonly measured by changes in the visual field (VF). This article focuses on the analysis of glaucoma progression using standard automated perimetry (SAP), which remains the gold standard for monitoring functional loss. We review key methods for detecting VF progression, including event-based and trend-based analyses, with particular attention to tools such as Guided Progression Analysis (GPA) and pointwise linear regression (PLR). The strengths and limitations of global indices like Mean Deviation (MD) and Visual Field Index (VFI) are discussed, along with the impact of test variability, learning effects, and the influence of disease stage. Emerging techniques, including machine learning models and deep learning approaches, are evaluated for their potential to enhance sensitivity and specificity in early detection ofVF deterioration. This article aims to provide clinicians and researchers with a detailed overview of current strategies and future directions in visual field-based glaucoma progression analysis.