Temporal Eye Data Analysis: Enhancing Ophthalmic Health Diagnostics with Recurrent Neural Networks

In order to improve ophthalmic health diagnoses, this research supports the use of Recurrent Neural Networks (RNNs) to assess temporal sequences of eye data. Reducing healthcare costs and improving patient outcomes are dependent on early detection and precise diagnosis of eye problems. Taking advantage of deep learning, namely RNNs, offers a viable method for identifying dynamic patterns and temporal relationships in eye data. This work intends to create an RNN-based model that can reliably identify different eye disorders using sequential data, such as photographs, sensor measurements, and patient records. It will accomplish this by conducting a thorough literature research and creative investigation of groundbreaking designs. Dynamic attention mechanisms are incorporated into the suggested model to improve interpretability and diagnostic precision. Standard measures are used in the model's validation and assessment to guarantee its dependability in clinical settings with 88% accuracy, 85% sensitivity, 90% specificity, 82% precision, an F1 score of 0.83, and an AUC-ROC of 0.91, the constructed RNN-based model exhibits noteworthy performance measures. The model's ability to detect eye diseases, reduce false positives, and strike a compromise between sensitivity and specificity is demonstrated by these results. The results underscore the potential of the model to transform the field of ophthalmic health diagnostics by providing sophisticated instruments for timely intervention and customized treatment plans, hence enhancing patient care and results