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This study explores the feasibility of quantitative Optical Coherence Tomography Angiography (OCTA) features translated from OCT using generative machine learning (ML) for characterizing vascular changes in retina. A generative adversarial network framework was employed alongside a 2D vascular segmentation and a 2D OCTA image translation model, trained on the OCT-500 public dataset and validated with data from the University of Illinois at Chicago (UIC) retina clinic. Datasets are categorized by scanning range (Field of view) and disease status. Validation involved quality and quantitative metrics, comparing translated OCTA (TR-OCTA) with ground truth OCTAs (GT-OCTA) to assess the feasibility for objective disease diagnosis. In our study, TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution and contrast quality, moderate structural similarity compared to GT-OCTAs). Vascular features like tortuosity and vessel perimeter index exhibits more consistent trends compared to density features which are affected by local vascular distortions. For the validation dataset (UIC), the metrics show similar trend with a slightly decreased performance since the model training was blind on UIC data, to evaluate inference performance. Overall, this study presents a promising solution to the limitations of OCTA adoption in clinical practice by using vascular features from TR-OCTA for disease detection. By making detailed vascular imaging more widely accessible and reducing reliance on expensive OCTA equipment, this research has the potential to significantly enhance the diagnostic process for retinal diseases.

The latest cellular technology, known as 5G-NR, is intended to significantly speed up and improve the effectiveness of wireless systems. A revolution in the telecom industry has been sparked by the widespread use of and increased reliance on cellular communication technology. Moreover, 5G and B5G technologies are expected to utilize an even higher-frequency range to achieve faster data transmission and lower latency communication. Consequently, while transmitting signals across various types of equipment and infrastructure, the general public is exposed to much higher frequencies of electromagnetic radiation. The increasing need for 5G NR base stations (gNodeB) has heightened public anxiety over potential negative health impacts. This study reviews recent research on the effects of electromagnetic waves on humans, particularly focusing on how these effects influence cognitive functions. Most research to date has not found significant differences in cognitive performance due to ubiquitous mobile communications. However, current research has largely been limited to 4G technologies, and the health effects of exposure to 5G user equipment (UE) and base stations in higher-frequency bands remain unexplored. If subsequent research suggests that exposure to high-frequency wireless networks significantly impacts cognitive functions, the deployment and acceptance of these technologies may face challenges and constraints. Therefore, such investigations are crucial for determining whether next-generation technologies pose no risk to individuals.