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评估细胞性估计方法:比较人工智能计数与病理学家的视觉估计.

Tomoharu Kiyuna1, Eric Cosatto2, Kanako C Hatanaka3

  • 1Healthcare Life Science Division, NEC Corporation, Tokyo 108-8556, Japan.

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概括
此摘要是机器生成的。

与病理学家相比,人工智能 (AI) 模型显著提高了肺癌样本中瘤含量比率 (TCR) 评估准确度. 这种人工智能驱动的方法提高了基因测试的精度,以获得更好的患者结果.

关键词:
在U-Net模型中,人工智能 (AI) 是一种人工智能.域名转移 域名转移 域名转移下一代测序 (NGS) 是指下一代的测序.网站依赖性 网站依赖性瘤含量比率 (TCR) 是指瘤含量的比率.

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科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 人工智能在瘤学中的应用

背景情况:

  • 下一代测序 (NGS) 通过识别癌症驱动基因变异来推动精准医学.
  • 精确的基因检测依赖于足够的瘤细胞,需要精确的瘤含量比率 (TCR) 评估.
  • 从H&E图像中估计TCR的病理学家变异性是一个重大挑战.

研究的目的:

  • 开发和验证一个AI模型,从H&E染色的肺癌图像中准确计算TCR.
  • 为了比较基于AI的TCR估计与病理学家视觉评估的准确性.
  • 评估开发的AI模型的稳定性和机构间的一致性.

主要方法:

  • 在41例肺癌病例中,通过3位病理学家对细胞进行详尽的标记,建立了TCR的"黄金标准".
  • 开发了一个紧的,快速的,完全卷积的神经网络AI模型用于细胞检测和分类.
  • 在13位病理学家和人工智能模型之间比较TCR准确度与黄金标准.

主要成果:

  • 人工智能模型实现了92%的细胞检测和84%的分类准确度.
  • 与病理学家视觉评估相比,基于AI的TCR计算显示出明显较低的误差 (p<0.05).
  • 人工智能模型表现出比平均病理学家评估更大的机构间稳定性.

结论:

  • 人工智能模型在临床环境中显著提高了瘤细胞性评估的准确性.
  • 通过人工智能增强的TCR准确性有助于更有效,更可靠的基因测试.
  • 采用强大的AI工具有望通过精确的癌症治疗改善患者的治疗结果.