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Artificial intelligence has been used effectively in medical diagnosis. The objective of this project is to examine the application of a collective AI model using weighted fusion of predicted probabilities from different AI architectures to diagnose various retinal conditions based on optical coherence tomography (OCT). A publicly available Noor dataset, comprising 16,822, images from 554 retinal OCT scans of 441 patients, was used to predict a diverse spectrum of age-related macular degeneration (AMD) stages: normal, drusen, or choroidal neovascularization. These predictions were compared with predictions from ResNet, EfficientNet, and Attention models, respectively, using precision, recall, F1 score, and confusion matric and receiver operating characteristics curves. Our collective model demonstrated superior accuracy in classifying AMD compared to individual ResNet, EfficientNet, and Attention models, showcasing the effectiveness of using trainable weights in the ensemble fusion process, where these weights dynamically adapt during training rather than being fixed values. Specifically, our ensemble model achieved an accuracy of 91.88%, precision of 92.54%, recall of 92.01%, and F1 score of 92.03%, outperforming individual models. Our model also highlights the refinement process undertaken through a thorough examination of initially misclassified cases, leading to significant improvements in the model's accuracy rate to 97%. This study also underscores the potential of AI as a valuable tool in ophthalmology. The proposed ensemble model, combining different mechanisms highlights the benefits of model fusion for complex medical image analysis. [ABSTRACT FROM AUTHOR]

Background and Objectives: Standard Full-field Electroretinography (SFE) is an invaluable procedure for evaluating the function of the retinal photoreceptors, pigmented epithelium and Müller cells. It is consists of scotopic, photopic, maximal response, oscillatory potential and 30 Hz flicker responses. Standardization of the results is a crucial step in interpreting the results efficiently and facilitates the process of the treatment. The aim of this study was to determine the normative data base of SFE method in Tabriz, IRAN. Materials and Methods: Eighty-five healthy individuals (with the age of 1st to 7th decades) who were resident in Tabriz city, were randomly selected and SFE was performed with DTL electrodes according to the ISCEV instruction. Results: The mean ages of male and female participants were 35.6±19.7 and 35.3±14.3 years, respectively (P=0.95). Conclusion: The standardization of the results of electroretinography is mandatory in any ophthalmology center to diagnose the relevant diseases and manage them properly. [ABSTRACT FROM AUTHOR]

Background and Objectives: Wet type Age-related Macular Degeneration (Wet type AMD) is a debilitating ocular disease which generally occurs among elderly population. Tumor Necrosis Factor alpha (TNF- ) is a reactive phase protein which is associated with Alzheimer, atherosclerosis, type 2 diabetes mellitus and aging. Materials and Methods: In this case-control study, 40 eyes of 40 patients with wet type age-related macular degeneration and 49 normal eyes of 49 healthy people were randomly selected and serum levels of TNF- , lipid profile and fasting blood glucose were checked. Results: The difference of mean ages and male to female ratio were not statistically significant (P=0.441, P=0.091). Serum level of TNF- was significantly higher among patients with retinal degeneration (Median (IQR) = 0.2 (0.1-0.58) vs. 0.1 (0-0.2); P=0.002). Conclusion: TNF- would have a role in the pathogenesis of the wet type age-related macular degeneration and pharmaceutical and/or non-pharmaceutical interventions might decelerate or even cease the progression of this ominous disease. [ABSTRACT FROM AUTHOR]