基于深度学习的模型,用于临床前药物安全性评估
Guillaume Jaume1,2,3,4, Simone de Brot5,6,7, Andrew H Song1,2,3,4
1Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
bioRxiv : the preprint server for biology
|August 2, 2024
概括
人工智能 (AI) 通过自动化毒理病理学来提高药物安全性评估. 在数以百万计的图像上训练的TRACE模型准确地描述了化合物毒性,并且在一致性方面超过了人类病理学家.
科学领域:
- 毒理学 毒理学 毒理学
- 计算病理学计算病理学
- 药物开发 药物开发
背景情况:
- 在临床前研究中评估药物毒性对于临床试验的进展至关重要.
- 动物组织的手动组织病理学分析是耗时的,并且会有变化.
- 人工智能 (AI) 提供了加速和更客观的病理评估的潜力.
研究的目的:
- 引入TRACE,一种用于自动化毒理性肝脏组织病理学评估的AI模型.
- 为了证明TRACE在处理有限的标记数据的各种诊断任务方面的能力.
- 建立一个新的计算框架,以加速毒理病理学.
主要方法:
- 开发了TRACE,这是一个人工智能模型,在1500万张来自 *Rattus norvegicus * * 临床前研究的基因病理图像上进行了训练.
- 利用来自157项临床前研究的数字化组织切片.
- 在包括响应评估,严重性评分,形态检索和剂量-响应特征在内的任务上评估了TRACE.
主要成果:
- TRACE成功地完成了多项毒理学任务,包括自动剂量反应表征.
- 在一项独立的读者研究中,TRACE与专家共识的一致性比平均兽医病理学家更高.
- 人工智能模型在各种诊断尺度和数据限制中表现出熟练.
结论:
- TRACE代表了计算毒理学的重大进步,提供了一种自动化和加速的方法.
- 人工智能框架提高了毒理病理学评估的一致性和可靠性.
- 这项技术有望通过提高诊断效率来加快药物开发过程.
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