Relationship between the morphology of the foveal avascular zone, retinal structure, and macular circulation in patients with diabetes mellitus

Diabetic Retinopathy (DR) is an extremely severe and common degenerative disease. The purpose of this study was to quantify the relationship between various parameters including the Foveal Avascular Zone (FAZ) morphology, retinal layer thickness, and retinal hemodynamic properties in healthy controls and patients with diabetes mellitus (DM) with and with no mild DR (MDR) using Spectral-Domain Optical Coherence Tomography (Spectralis SDOCT, Heidelberg Engineering GmbH, Germany) and the Retinal Function Imager (Optical Imaging, Ltd., Rehovot, Israel). Our results showed a higher FAZ area and diameter in MDR patients. Blood flow analysis also showed that there is a significantly smaller venous blood flow velocity in MDR patients. Also, a significant difference in roundness was observed between DM and MDR groups supporting the development of asymmetrical FAZ expansion with worsening DR. Our results suggest a potential anisotropy in the mechanical properties of the diabetic retina with no retinopathy that may trigger the FAZ elongation in a preferred direction resulting in either thinning or thickening of intraretinal layers in the inner and outer segments of the retina as a result of autoregulation. A detailed understanding of these relationships may facilitate earlier detection of DR, allowing for preservation of vision and better clinical outcomes

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment