深度学习模型以高准确度捕捉克罗恩病和性结肠炎中的组织学疾病活动
Dawid Rymarczyk1,2, Weiwei Schultz3, Adriana Borowa1,2
1AI Lab, Ardigen SA, Kraków, Poland.
Journal of Crohn's & colitis
|October 9, 2023
概括
深度学习模型准确地评估炎症性肠病 (IBD) 中的组织学疾病活动,帮助药物开发. 这些人工智能工具与克罗恩病 (CD) 和性结肠炎 (UC) 的专家病理学家有着强烈的共识.
科学领域:
- 胃肠病学 胃肠病学
- 计算病理学计算病理学
- 人工智能的人工智能
背景情况:
- 组织学疾病活动对临床结果和炎症性肠病 (IBD) 药物开发至关重要.
- 在IBD中自动化组织学评估对于有效和客观的评估至关重要.
研究的目的:
- 开发和评估用于自动化IBD组织学评估的深度学习模型.
- 为了比较不同的AI方法在预测组织学疾病活动得分方面的表现.
主要方法:
- 来自克罗恩病 (CD) 和性结肠炎 (UC) 临床试验的组织学图像被用于训练AI模型.
- 模型预测了CD的全球组织学活动得分 (GHAS) 和UC的Geboes得分.
- 人工智能模型的预测与专家中央读数和独立病理学家进行了比较.
主要成果:
- 一个基于自我注意的多实例学习模型 (SA-AbMILP) 显示出卓越的性能.
- 人工智能预测的GHAS和Geboes亚等级与中央读数 (65-89%的准确性) 显示出中等至实质性的一致.
- 该模型准确地区分了CD和UC在不同解剖位置的组织特征 (76-94%的准确性).
结论:
- 深度学习模型有效地根据已建立的评分系统区分IBD中的微观疾病活动.
- 人工智能驱动的组织学评估显示了与人类病理学家可比的准确性,支持其在临床试验中的使用.
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