An Effective Framework for Disease Progression Prediction through Temporal Analysis and Medical Image Synthesis

This research introduces an effective approach for predicting disease progression by analyzing medical data from X-ray images and temporal indicators. The framework extracts features from medical images using Convolutional Neural Networks (CNN) and analyzes them over time with Long Short-Term Memory (LSTM) networks to detect disease patterns. The model predicts future disease trajectories, such as deterioration, stability, or improvement. It integrates temporal analysis and image generation by using LSTM outputs as inputs to a Generative Adversarial Network (GAN), generating synthetic medical data to simulate potential future disease states. Two feedback mechanisms—an image quality discriminator and a temporal critic—refine the generated predictions, enhancing their accuracy. The Dynamic Dual-Feedback GAN (DDF-GAN) significantly outperforms MedGAN, TimeGAN, and CGAN-LSTM across various evaluation metrics, including Fréchet Inception Distance (FID), Structural Similarity Index (SSIM), and Mean Absolute Error (MAE). It achieves notable improvements, with 20% in FID, 15% in SSIM, and 12% in MAE, while also demonstrating 25% better performance in Time-wise Mean Squared Error (MSE). DDF-GAN excels in predictive accuracy, image quality, and temporal consistency, making it a superior choice for medical data modeling.