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数字病理学对非酒精性脂肪肝炎的评估

Arun J Sanyal¹, Prakash Jha², David E Kleiner³

¹Stravitz-Sanyal Institute for Liver Disease and Metabolic Health, Virginia Commonwealth University School of Medicine, Richmond, VA, USA. arun.sanyal@vcuhealth.org.

Nature reviews. Gastroenterology & hepatology

|October 3, 2023

概括

对非酒精性脂肪性肝病 (NAFLD) 和非酒精性脂肪性肝炎 (NASH) 的组织学评估面临挑战. 机器学习和数字工具为评估药物疗效和临床评估提供了有前途的进展.

科学领域:

肝病学肝病学是一种肝病学.
数字病理学数字病理学
人工智能在医学中的应用

背景情况:

组织学评估是评估非酒精性脂肪肝炎 (NASH),一种严重形式的非酒精性脂肪肝疾病 (NAFLD) 的黄金标准.
传统的组织学有局限性,阻碍了NASH的药物开发.
机器学习和数字病理学的进步正在出现,以解决这些局限性.

研究的目的:

审查传统的NASH评估及其局限性.
讨论用于NAFLD肝脏组织学评估的数字和机器学习方法.
探索这些工具在NASH药物开发和临床实践中的应用.

主要方法:

对NASH当前常规组织学评估系统的审查.
讨论新兴的数字病理学工具和用于肝脏组织学的机器学习算法.
对评估NASH药物开发中的治疗疗效的主要工具的分析.

主要成果:

传统的组织学具有影响药物开发的局限性.
数字和机器学习方法显示了客观和高效的NASH评估的潜力.
正在开发和验证用于治疗疗效研究的特定工具.

结论:

数字病理学和机器学习为克服NASH组织学评估的局限性提供了重大潜力.
这些技术可以加强药物开发,并支持NAFLD的常规临床评估.
需要进一步的研究才能将这些进展完全转化为临床实践.

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