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基于人工智能的模型用于在全幻灯片图像中诊断Helicobacter pylori

Kehan Teng¹, Lihua Ren², Xiaoyu Yan³

¹Department of Pathology, Zhongda Hospital, Southeast University School of Medicine, Nanjing, Jiangsu, China.

Frontiers in medicine

|June 26, 2025

概括

人工智能 (AI) 模型现在可以高精度地检测Helicobacter pylori (H. pylori) 感染. DS-MIL模型的表现优于高级病理学家,提高了H. pylori检测的诊断效率和准确性.

科学领域:

数字病理学数字病理学
医疗人工智能的人工智能
胃肠病学胃肠病学

背景情况:

杆菌 (H. pylori) 感染是胃癌和慢性胃炎的主要原因.
通过H&E染色进行传统的H. pylori诊断是劳动密集型的,容易出现错误.
需要更准确,更有效的诊断方法.

研究的目的:

开发和评估人工智能 (AI) 模型,用于在组织学图像中自动检测H. pylori.
将人工智能模型的诊断性能与高级病理学家进行比较.
为了提高H. pylori诊断的准确性和效率.

主要方法:

开发了三个多级学习 (MIL) 模型 (AB-MIL,DS-MIL,Trans-MIL).
1,020个数字化全幻灯片图像 (WSIs) 用于培训,验证和测试.
计算性能指标 (精度,AUC,灵敏度,特异性) 并与病理学家诊断进行比较.

主要成果:

这三种MIL模型都显示出H. pylori检测的强大诊断性能.
DS-MIL模型获得了最高的精度 (89.7%) 和AUC (0.949),超过了高级病理学家 (81.7%).
与人类专家相比,DS-MIL表现出更高的灵敏度和特异性.

结论:

基于人工智能的预测模型,特别是DS-MIL,显著提高了H. pylori的诊断准确性和临床效率.
开发的AI模型提供了一个有前途的工具来帮助病理学家检测H. pylori.
多中心验证正在进行中,以确保模型的通用性.

关键词:

在Helicobacter Pylori的研究中,人工智能的人工智能是人工智能.慢性胃炎慢性胃炎胃粘膜活检胃粘膜活检病理诊断病理诊断病理诊断

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