About this Research Topic
Glaucoma is the leading cause of irreversible visual loss, driven by a neurodegenerative process that affects the ganglion cells and their axons, leading to remodeling and excavation of the optic nerve head. This structural damage has significant functional repercussions, impairing the visual field, affecting daily activities, and potentially resulting in blindness. Therefore, effectively monitoring glaucoma to detect progression and determine its rate is critical.
Today, numerous technologies, applications, and tools enable clinicians to detect and monitor glaucoma progression effectively and promptly. Functionally, visual field testing, and structurally, optical coherence tomography (OCT), are the primary devices used in clinical practice. These devices provide parameters such as retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), and optic nerve head morphometry, which are tracked over time to observe deterioration in glaucomatous eyes.
However, there are challenges in detecting progression, such as identifying fast progressors and evaluating eyes with special phenotypes, including high myopia. Novel parameters and applications could further enhance the glaucoma monitoring toolbox. Additionally, the integration of artificial intelligence algorithms holds the potential to significantly improve the clinical capabilities of progression detection and represents an important area of study.
This research topic aims to incorporate advancements in the field of glaucoma progression by utilizing both clinically established devices and parameters, as well as novel applications, parameters, and approaches. We invite original research articles, full review articles, mini-review articles, and editorials that enhance current knowledge and provide new insights for developing new strategies in the management of glaucoma.
Today, numerous technologies, applications, and tools enable clinicians to detect and monitor glaucoma progression effectively and promptly. Functionally, visual field testing, and structurally, optical coherence tomography (OCT), are the primary devices used in clinical practice. These devices provide parameters such as retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), and optic nerve head morphometry, which are tracked over time to observe deterioration in glaucomatous eyes.
However, there are challenges in detecting progression, such as identifying fast progressors and evaluating eyes with special phenotypes, including high myopia. Novel parameters and applications could further enhance the glaucoma monitoring toolbox. Additionally, the integration of artificial intelligence algorithms holds the potential to significantly improve the clinical capabilities of progression detection and represents an important area of study.
This research topic aims to incorporate advancements in the field of glaucoma progression by utilizing both clinically established devices and parameters, as well as novel applications, parameters, and approaches. We invite original research articles, full review articles, mini-review articles, and editorials that enhance current knowledge and provide new insights for developing new strategies in the management of glaucoma.
Keywords: Glaucoma, neurodegenerative process, structural damage, visual field, Detection
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