Deep learning techniques have emerged as powerful tools for medical image diagnosis. However, their application in non-invasive diagnosis remains largely unexplored. Given the limitations of liver biopsy, there is a pressing need for non-invasive methods to accurately diagnose MASH. To address this gap, we aim to develop a deep learning–based, non-invasive, fusion-guided multimodal tool for MASH diagnosis.