In a study published in Nature Medicine, researchers report an artificial intelligence (AI)-based digital pathology tool for scoring metabolic dysfunction-associated steatohepatitis (MASH; formerly known as nonalcoholic steatohepatitis (NASH)) histology. MASH clinical trial enrollment and endpoint assessment are based on histological criteria. The tool, which is termed AIM-MASH, produced predictions for steatosis grade and fibrosis stage that were comparable to consensus MASH Clinical Research Network grading and staging. The aim of the tool is to reduce variability in interpretation for trial outcomes, improve sensitivity of scoring systems and assist pathologists in histological review of clinical trials for MASH.

在《自然医学》杂志上发表的一项研究中，研究人员报告了一种基于人工智能 (AI) 的数字病理学工具，用于对代谢功能障碍相关脂肪性肝炎 (MASH；以前称为非酒精性脂肪性肝炎 (NASH)) 组织学进行评分。 MASH 临床试验入组和终点评估基于组织学标准。该工具被称为 AIM-MASH，对脂肪变性等级和纤维化阶段进行预测，与 MASH 临床研究网络共识分级和分期相当。该工具的目的是减少试验结果解释的变异性，提高评分系统的敏感性，并协助病理学家对 MASH 临床试验进行组织学审查。