Machine Learning-Based Prediction and Remedy Assessment for Non-Alcoholic Fatty Liver Disease Via Ultrasound Imaging

Abstract

The development of effective treatment strategies is essential due to the prevalence of non-alcoholic fatty liver disease (NAFLD) worldwide. Purpose: In this study, the ML methodology on data retrieved from a multicenter ERCP database is used to evaluate and predict the effect of different therapeutic modalities for NAFLD to maximize personalizing individual treatment regimens and response management. Method: Using a large, rich dataset of biochemical biomarkers, clinical attributes, and patient demographics, this study leverages several state-of-the-art machine learning models, in particular, the VGG16 model, to detect patterns and variables related to success in treatment automatically. Result: show that the VGG16-based strategy significantly outperforms other methods in determining the success of therapy, stratifying non-alcoholic steatohepatitis (NASH) by severity of grade: I (5–33%) in mild steatosis, II (34–66%) in medium severe steatosis, and III (>66%). Aggressive Stromal Response estate costs facilitate the use of prognostic factors to predict recurrence after surgery. Conclusion: AI-based strategies can enhance understanding of NAFLD management and enable the creation of personalized nutritional and therapeutic interventions. The integration of artificial intelligence with NAFLD management appears promising, offering fresh insights into treatment algorithms for prevalent chronic liver disease. Future scope: This research should focus on expanding the dataset and adding more parameters to enhance the generalizability and refinement of prediction models.