Development of AI Based Fibrosis Detection Algorithm by SHG/TPEF Microscopy for Fully Quantified Liver Fibrosis Assessment in MASH

ABSTRACT Background and Aims Metabolic dysfunction‐associated steatotic liver disease (MASLD) is a major global cause of chronic liver disease, with the potential to progress from steatosis to metabolic dysfunction‐associated steatohepatitis (MASH) and cirrhosis. Fibrosis is a key determinant of liver‐related morbidity and mortality, highlighting the need for precise, reproducible assessment methods. This study aimed to develop and validate an Artificial Intelligence (AI)‐based fibrosis detection algorithm using Second Harmonic Generation/Two Photon Excitation Fluorescence (SHG/TPEF) microscopy. Methods The algorithm integrates SHG/TPEF microscopy, which uses ultra‐fast lasers to capture intrinsic optical signals from unstained liver biopsies, with Machine Learning (ML)‐based image analysis. The resulting qFibrosis model quantifies collagen morphology to generate a continuous fibrosis index. Results A standardised workflow was established, encompassing sample acquisition, SHG/TPEF imaging, region‐specific analysis and collagen feature quantification. Each step of the AI‐based ML of qFibrosis algorithm used to assess and quantify liver fibrosis is described in detail in this study. Conclusions This AI‐driven approach enables accurate, continuous quantification of liver fibrosis, overcoming the variability of traditional histopathology. The qFibrosis model has potential as a standardised tool for therapeutic evaluation and disease monitoring in MASLD/MASH, representing a significant advancement in liver fibrosis assessment.