一个基于多站点融合的深度卷积神经网络,用于使用内镜图像对Helicobacter pylori感染状况进行分类:一个多中心研究
Duwei Dai1,2, Xiaojing Quan1, Yueqin Zheng1
1Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Journal of gastroenterology and hepatology
|July 19, 2025
概括
一个新型的深卷积神经网络 (DCNN) 准确地使用多部位胃图像对Helicobacter pylori (Hp) 感染状况进行分类. 这种人工智能模型在诊断当前和过去的HP感染方面优于内镜医生.
科学领域:
- 医疗人工智能 医疗人工智能
- 胃肠病学成像成像 胃肠病学成像
- 计算病理学计算病理学
背景情况:
- Helicobacter pylori (Hp) 感染是一个重大的全球健康问题.
- 准确诊断HP感染状态 (未感染,过去,当前) 对于有效治疗至关重要.
- 目前的诊断方法有局限性,需要先进的方法.
研究的目的:
- 开发一个深层卷积神经网络 (DCNN) 用于对HP感染状态进行分类.
- 整合来自多个胃部位的功能,以提高诊断准确度.
- 为了比较单站式与多站式DCNN模型和专家内镜师的疗效.
主要方法:
- 十个深度学习架构在8个中心的676个受试者的3380个白光图像上接受了训练.
- 外部验证和测试是在单独的数据集上进行的.
- 开发和比较了单站点和多站点融合DCNN模型,包括基于投票的多站点融合方法.
主要成果:
- 性能最高的单站点DCNN模型 (Wide-ResNet) 在所有感染类别中实现了高精度和AUC.
- 基于投票的多站点融合DCNN模型显示出卓越的准确性和AUC,特别是在非感染和当前感染方面.
- 与经验丰富的内镜师相比,DCNN模型显示了增强的灵敏度,特异性和精度.
结论:
- 一个基于投票的多站点融合DCNN模型有效地分类Hp感染状态.
- 开发的DCNN在区分未感染和目前感染的个体方面表现出色.
- 这种人工智能驱动的方法为HP感染诊断提供了一个有前途的工具.
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