Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endpoint assessment, suffers from high reader variability. This study represents the most comprehensive multisite analytical and clinical validation of an artificial intelligence (AI)-based pathology system, AI-based measurement of metabolic dysfunction-associated steatohepatitis (AIM-MASH), to assist pathologists in MASH trial histology scoring. AIM-MASH demonstrated high repeatability and reproducibility compared to manual scoring. AIM-MASH-assisted reads by expert MASH pathologists were superior to unassisted reads in accurately assessing inflammation, ballooning, MAS ≥ 4 with ≥1 in each score category and MASH resolution, while maintaining non-inferiority in steatosis and fibrosis assessment. These findings suggest that AIM-MASH could mitigate reader variability, providing a more reliable assessment of therapeutics in MASH clinical trials.

代谢功能障碍相关脂肪性肝炎 （MASH） 是肝脏相关发病率和死亡率的主要原因，但治疗选择有限。肝活检的手动评分是目前临床试验入组和终点评估的黄金标准，存在很高的读者差异性。本研究代表了基于人工智能 （AI） 的病理学系统、基于 AI 的代谢功能障碍相关脂肪性肝炎测量 （AIM-MASH） 的最全面的多站点分析和临床验证，以协助病理学家进行 MASH 试验组织学评分。与手动评分相比，AIM-MASH 表现出较高的可重复性和再现性。由专业 MASH 病理学家提供的 AIM-MASH 辅助读数在准确评估炎症、气球样变、MAS ≥ 4 方面优于无辅助读数，每个评分类别和 MASH 分辨率均为 ≥1，同时在脂肪变性和纤维化评估中保持非劣效性。这些发现表明 AIM-MASH 可以减少读者的变异性，从而对 MASH 临床试验中的治疗提供更可靠的评估。