计算性病理学的进步
David L Hölscher1, Roman D Bülow2, Martin Strauch2
1Institute for Pathology, Rheinisch-Westfälische Technische Hochschule Aachen University Hospital, Aachen, Germany; Department for Nephrology and Clinical Immunology, Rheinisch-Westfälische Technische Hochschule Aachen University Hospital, Aachen, Germany.
Kidney international
|September 21, 2025
概括
使用人工智能的计算病理学,通过自动化图像分析增强了病理诊断. 临床整合仍然面临挑战,但未来的发展有望改变这一领域.
科学领域:
- 腎病理學 腎病理學
- 计算病理学计算病理学
- 数字病理学数字病理学
背景情况:
- 传统的病理学依赖于定性和半定量评估.
- 人工智能和深度学习的进步正在改变病理学.
- 计算病理学将计算方法集成到数字病理学工作流程中.
研究的目的:
- 为了提供一个全面的概述计算病理学在病理学的进展.
- 讨论方法论方法,它们的优点和局限性.
- 突出病理学中计算病理学的挑战和未来方向.
主要方法:
- 定量性病理学 (病理学)
- 深度学习用于图像分类和回归.
- 自动化决策支持系统
主要成果:
- 计算病理学可实现对组织病理学图像的自动化,高通量分析.
- 方法提高了诊断的精度,可重复性和效率.
- 病理学和深度学习等各种方法显示出显著的潜力.
结论:
- 计算病理学正在重塑脏病理学诊断.
- 临床实施必须解决技术,监管和伦理方面的挑战.
- 该领域正进入一个令人兴奋的时代,有可能发生重大变革.
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