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自动评估Ki-67标记指数使用细胞水平检测和分类在全片图像中的自动评估.

Masayuki Tsuneki¹, Meng Li¹, Fahdi Kanavati¹

¹Medmain Research, Medmain Inc., 2-4-5-104, Akasaka, Chuo-ku, Fukuoka 810-0042, Japan.

Diagnostics (Basel, Switzerland)

|March 14, 2026

概括

一个人工智能 (AI) 系统自动化 Ki-67 标记指数 (LI) 评估,提高瘤增殖标记的可重复性. 这种人工智能工具的性能与专家病理学家相美,有助于例行组织病理学.

科学领域:

在瘤学瘤学.
病理学病理学病理学
人工智能的人工智能

背景情况:

Ki-67标记指数 (LI) 对于评估瘤扩散至关重要.
手动Ki-67 LI评估是耗时的,并且容易引起观察者之间的显著变化.
需要自动化方法来提高临床实践中的可复制性.

研究的目的:

评估基于人工智能的系统,用于自动化,细胞级 Ki-67 LI 评估.
将人工智能系统的性能与专家病理学家进行比较.
确定AI在Ki-67 LI评估中的临床相关性.

主要方法:

开发了一个使用卷积神经网络进行细胞级核分类的AI系统 (Ki-67阳性/阴性).
利用了先前存在的细胞检测模型进行核鉴定.
被训练并将AI分类器应用于由三个病理学家独立评估的组织病理病例.

主要成果:

在一个大型试验组中,AI细胞分类实现了98%的AUC.
人工智能系统与专家病理学家的一致性与人类观察者之间的变异性相似.
人工智能驱动的Ki-67 LI评估在各种扩散水平上显示出高准确度.

关键词:

在这里,我们可以看到AIAIAI.在 Ki-67 机器人这是分类分类的分类.检测检测检测检测检测标签指数标签指数病理学的病理学

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结论:

细胞级自动化Ki-67评估具有改善诊断一致性的巨大潜力.
人工智能系统可以在常规基因病理学中作为可重复的决策支持工具.
由人工智能驱动的Ki-67 LI分析为手动评分提供了可靠的替代方案.