Refractive Error

Refractive error, the most common cause of correctable vision impairment, is a focus of intense research aiming to improve diagnosis and management. Current efforts leverage machine learning, particularly convolutional neural networks and deep learning models, to analyze fundus images and other visual data for accurate prediction of myopia progression and refractive error, often incorporating axial length measurements and advanced techniques like copula modeling to improve prediction accuracy. These advancements offer the potential for earlier intervention, reduced healthcare costs, and improved accessibility of eye care through automated screening and remote diagnosis, particularly for populations with limited access to specialists. Furthermore, research is exploring novel computational methods, such as neural radiance fields and eikonal fields, to model and synthesize images of refractive objects, improving virtual and augmented reality experiences and advancing 3D reconstruction techniques.