Prediction of Alzheimer's Disease Progression Using Longitudinal Brain MRI Data and GANs

This research introduces a new method for predicting the course of Alzheimer's disease by combining longitudinal brain MRI data with Generative Adversarial Networks (GANs). Using additional information from reputable repositories, we carried out a descriptive study by applying a deductive technique and an interpretivist mindset. The GAN exhibited remarkable efficacy in producing lifelike MRI pictures by accurately identifying minute structural alterations that signify the advancement of the disease. The predictive model outperformed previous techniques with its high sensitivity and specificity. Comparison with well-established methods demonstrated our GAN-based approach's greater accuracy. Clinical validation, ethical issues, and multi-modal data integration should be the main areas of future research. This novel approach has the potential to completely transform Alzheimer's disease early detection and treatment programs.