在胃活检中使用智能手机辅助检测系统对Helicobacter Pylori进行深度学习
Guanmeng Gao1,2, Zihan Wei3,4, Fei Pei3
1School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种人工智能系统,用于在胃活检中检测Helicobacter pylori (HP),达到89.23%的准确性. 智能手机辅助工具提高了病理学家的诊断一致性和准确性,标志着HP查的重大进步.
科学领域:
- 胃肠病学 胃肠病学
- 医学诊断 医学诊断 医学诊断
- 人工智能的人工智能
背景情况:
- 杆菌 (HP) 慢性感染全球一半以上的人口,导致胃炎,和胃癌.
- 手动显微镜检测HP是耗时且容易出现错误的,由于病理学家疲劳,可能会错过高达14%的阳性病例.
研究的目的:
- 开发和评估深度学习框架,用于在胃活检中自动检测HP.
- 评估实时,人工智能辅助诊断系统对病理学家在HP查中的准确性和一致性的影响.
主要方法:
- 一个深度学习模型 (Faster-R-CNN与ResNet 50) 在270个胃活检样本上进行了训练和评估.
- 一个实时系统通过5G整合了显微镜,人工智能算法和智能手机,用于病理学家确认.
- 诊断的准确性和一致性在有和没有人工智能协助的病理学家之间进行了比较.
主要成果:
- 采用ResNet 50模型的Faster-R-CNN在自动检测HP时实现了89.23%的准确性.
- 实时人工智能系统通过为病变提供立即警报,提高了诊断一致性和准确性.
- 病理学家的诊断得到了人工智能系统的增强,这并没有增加观察时间或干扰他们的工作流程.
结论:
- 一个新的深度学习框架 (Faster-R-CNN与ResNet 50) 能够在胃活检中准确,自动检测HP.
- 这种智能手机辅助的实时人工智能系统是首个用于HP查的智能手机,提高了诊断可靠性.
- 这种人工智能工具与传统显微镜无集成,为病理学家提供了一种高效有效的方法来选Helicobacter pylori.
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