Analysis of the perfusion of the optic nerve using angio-OCT in glaucoma

Applications of optical coherence tomography angiography in glaucoma: current status and future directions

Glaucoma is a leading cause of irreversible blindness worldwide, with its pathophysiology remaining inadequately understood. Among the various proposed theories, the vascular theory, suggesting a crucial role of retinal vasculature deterioration in glaucoma onset and progression, has gained significant attention. Traditional imaging techniques, such as fundus fluorescein angiography, are limited by their invasive nature, time consumption, and qualitative output, which restrict their efficacy in detailed retinal vessel examination. Optical coherence tomography angiography (OCTA) emerges as a revolutionary imaging modality, offering non-invasive, detailed visualization of the retinal and optic nerve head microvasculature, thereby marking a significant advancement in glaucoma diagnostics and management. Since its introduction, OCTA has been extensively utilized for retinal vasculature imaging, underscoring its potential to enhance our understanding of glaucoma's pathophysiology, improving diagnosis, and monitoring disease progression. This review aims to summarize the current knowledge regarding the role of OCTA in glaucoma, particularly its potential applications in diagnosing, monitoring, and understanding the pathophysiology of the disease. Parameters pertinent to glaucoma will be elucidated to illustrate the utility of OCTA as a tool to guide glaucoma management.

Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy

Diabetic Retinopathy (DR) is one of the leading causes of blindness for people who have diabetes in the world. However, early detection of this disease can essentially decrease its effects on the patient. The recent breakthroughs in technologies, including the use of smart health systems based on Artificial intelligence, IoT and Blockchain are trying to improve the early diagnosis and treatment of diabetic retinopathy. In this study, we presented an AI-based smart teleopthalmology application for diagnosis of diabetic retinopathy. The app has the ability to facilitate the analyses of eye fundus images via deep learning from the Kaggle database using Tensor Flow mathematical library. The app would be useful in promoting mHealth and timely treatment of diabetic retinopathy by clinicians. With the AI-based application presented in this paper, patients can easily get supports and physicians and researchers can also mine or predict data on diabetic retinopathy and reports generated could assist doctors to determine the level of severity of the disease among the people.