AI+MASH is an integrated AI-based pathology analysis platform designed for metabolic dysfunction-associated steatohepatitis in mouse, built upon four core feature models covering steatosis, inflammation, hepatocyte degeneration, and fibrosis. Using deep learning-based image analysis, it automatically extracts and quantifies key pathological features from mouse liver, transforming them into structured, analyzable datasets. The system integrates a cloud-based interface, enabling users to log in and access study results in real time, providing a standardized foundation for murine fatty liver disease research.

Compared with traditional pathology that relies on manual observation and descriptive reporting, AI+MASH applies AI-driven image computation to convert subjective interpretation into objective quantitative data. Through simultaneous multi-feature analysis and quantitative data, it reduces inter-observer variability, improves consistency and reproducibility, and enables high-throughput analysis supported by high-performance computing.

AI+MASH significantly improves the efficiency of pathological analysis while delivering consistent and comparable quantitative results. It facilitates cross-study integration, enhancing the reliability and comparability of drug screen in preclinical study. Long-term accumulation of pathology data enables large-scale data-driven modeling for disease progression analysis and drug response prediction, forming a critical foundation for future biomedical research and precision medicine applications.