Abstract

Purtscher retinopathy is a rare eye disease but the condition that considerable challenge for medical professionals due to its subtle and unpredictable symptoms. In this study, were utilized an unexpected strategy, harnessing the power of deep learning techniques alongside Optical Coherence Tomography (OCT) images, to address this diagnostic dilemma. By compiling a comprehensive database of OCT images from patients afflicted with Purtscher retinopathy and those with healthy eyes, were embarked on a journey to train a sophisticated deep-learning model. The model, a cutting-edge computational system designed to recognize intricate patterns, underwent the right training to meticulously analyze the gathered images and distinguish between the two groups with precision. The results of the model were truly remarkable: The model's outstanding 97% accuracy rate was reached in correctly identifying cases of Purtscher retinopathy disease, surpassing the capabilities of traditional diagnostic methods by a significant margin. The groundbreaking advancement underscores the immense potential of deep learning in conjunction with OCT imaging for the early and precise diagnosis of Purtscher retinopathy. By the leveraging this innovative technology, that is not only enhancing understanding of this complex condition but also paving the way for more effective interventions and improved patient outcomes in the future.