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使用无密码人工智能模型对性结肠炎的组织学检测

Yuichiro Hamamoto1,2, Michihiro Kawamura3, Hiroki Uchida3

  • 1Department of Diagnostic Pathology, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan.

International journal of surgical pathology
|October 26, 2023
PubMed
概括

一个新的人工智能 (AI) 模型准确地识别了性结肠炎 (UC) 组织学模式. 这种AI工具有助于诊断UC和其他结肠疾病,解决病理学家短缺问题.

关键词:
腺癌瘤是一种腺癌.人工智能的人工智能是人工智能.炎症性肠病是一种炎症性肠病.机器学习是机器学习.性结肠炎是一种

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科学领域:

  • 胃肠病学 胃肠病学
  • 病理学 病理学 病理学
  • 人工智能的人工智能

背景情况:

  • 性结肠炎 (UC) 是一种慢性疾病,主要影响年轻人,需要准确的组织学诊断.
  • 诊断病理学家的短缺对及时和精确的UC诊断构成了挑战.
  • 组织学检查对于区分UC与其他结肠疾病和正常组织至关重要.

研究的目的:

  • 开发和评估一种人工智能 (AI) 模型,用于对性结肠炎 (UC) 的组织图像进行分类.
  • 评估AI模型区分UC与非UC结直肠炎,腺癌和对照样本的能力.
  • 展示无代码AI平台在病理学复杂诊断任务中的实用性.

主要方法:

  • 利用无代码的人工智能平台"可教机器"来训练一个分类模型.
  • 在5100张组织学图像上训练模型,包括UC,非UC结直肠炎,腺癌和对照.
  • 使用900张组织学图像的独立测试集验证了模型的性能.

主要成果:

  • 人工智能模型在所有类别中实现了高准确率:UC为0.99,非UC大肠炎为1.00,腺癌为0.99,对照为0.99.
  • 该模型表现出强大的能力来识别UC的独特组织学特征.
  • 这项研究代表了无代码人工智能平台的首次应用,用于UC中全面的组织学模式识别.

结论:

  • 一个无代码的人工智能平台可以有效地被训练,以准确区分性结肠炎的组织图像.
  • 开发的AI模型显示了支持病理学家诊断UC和相关疾病的巨大潜力.
  • 这种方法提供了一个可扩展和可访问的解决方案,以帮助诊断难以治疗的胃肠道疾病.