Supporting data for "Harnessing the Power of Retinal Nerve Fiber Layer Optical Texture Analysis and Deep Learning in Glaucoma"

This dataset supports the thesis "Harnessing the Power of Retinal Nerve Fiber Layer Optical Texture Analysis and Deep Learning in Glaucoma." The research aims to leverage RNFL Optical Texture Analysis (ROTA) and deep learning techniques for glaucoma detection, classification, and progression tracking. The thesis comprises three main components: pattern analysis of ROTA in early glaucoma subjects, analysis of fundus images for glaucomatous disease diagnosis, and analysis of sequential ROTA maps for progression monitoring. The dataset is organized across three chapters, where Chapter 2 contains Excel files with patient demographics and ROTA measurement parameters from early glaucoma cases, Chapter 3 includes fundus photographs for both development and evaluation of the glaucoma diagnosis foundation model, and Chapter 4 contains sequential ROTA images for developing and assessing the glaucoma progression AI model.