一般病理学家使用基于深度学习的虚拟染色来实现近专家的诊断性能,用于捐赠脏评估:一项回顾性-前性诊断一致性研究
Jin-Peng Cen1, Sheng-Dong Ge2, Yang-Shu Zhou3
1Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510080, Guangdong, PR China; Department of Urology, The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, 510900, Guangdong, PR China.
概括
人工智能虚拟染色通过提高一般病理学家的纤维化评估准确度来增强捐赠的评估. 这种人工智能工具弥合了专业知识的差距,使通用病理学家能够在评估病理方面达到专业水平的表现.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 数字病理学数字病理学
- 人工智能的人工智能
背景情况:
- 血素和欧 (H&E) 染色不充分可视化原纤维,阻碍了对捐赠脏的间歇性纤维化评估.
- 一般病理学家往往缺乏专门的培训,导致评估慢性病理的挑战.
研究的目的:
- 研究基于人工智能的虚拟染色在增强捐赠的评估,特别是对于间歇性纤维化和慢性病理的有效性.
- 为了确定虚拟染色是否可以提高诊断准确性和不同专业水平的病理学家之间的观察者间协议.
主要方法:
- 开发并验证了一个基于CycleGAN的人工智能模型,以将H&E图像转换为虚拟的马森三色 (VMT) 表示.
- 评估间歇性纤维化和慢性变化,使用Remuzzi评分系统与VMT和没有VMT,比较病理学家和通用病理学家的评估.
- 在冷部分进行了前性验证,以确认模型的性能.
主要成果:
- 虚拟马森的三色 (VMT) 有效地可视化了间歇性原纤维,使得纤维化更可靠的分类.
- 用VMT显著提高了诊断准确性,显著增加了一般和病理学家之间的观察者间一致性.
- 一般病理学家通过使用VMT在纤维化评估中实现了近乎专家的表现,弥合了专业知识的差距.
结论:
- 基于深度学习的虚拟染色显著提高了一般病理学家对捐赠的评估的准确性,接近专家水平的性能.
- 这项技术为在移植医学中评估纤维化和慢性病理提供了一种高效,具有成本效益的解决方案.
- 虚拟染色有可能消除通用病理学家和专业病理学家之间的诊断差异.
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