多阶段深度学习用于使用内镜图像对Helicobacter pylori感染状况进行分类
Guang Li1, Ren Togo2, Katsuhiro Mabe3
1Education and Research Center for Mathematical and Data Science, Hokkaido University, Sapporo, Japan.
Journal of gastroenterology
|January 15, 2025
概括
一种新的深度学习方法准确地分类了Helicobacter pylori感染状态,表现优于医生. 这一进步通过分析内镜图像,有助于胃癌查.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 对Helicobacter pylori感染状况的自动分类对于临床管理至关重要.
- 由于区分未感染,当前感染和根除后状态的复杂性,目前的方法在准确性方面面临挑战.
- 开发先进的AI工具对于提高诊断性能至关重要.
研究的目的:
- 开发和评估一种新的多阶段深度学习方法,用于H. pylori感染状态分类.
- 将深度学习方法的性能与人类医生进行比较.
- 探索AI在增强胃癌查方面的潜力.
主要方法:
- 一个多阶段的深度学习模型对538名受试者进行了训练,并对146名受试者进行了验证.
- 对未感染,当前感染和根除后的H. pylori状态进行了分类性能评估.
- 人工智能方法的准确性与四名医生的准确性进行了比较,包括患者病史和没有病史.
主要成果:
- 深度学习方法实现了高精度:87.7% (未感染),83.6% (根除后) 和95.9% (目前感染).
- 结合根除史进一步提高了准确性,达到92.5% (未感染),91.1% (根除后) 和98.6% (目前感染).
- 人工智能方法在所有类别中始终超过了医生.
结论:
- 拟议的多阶段深度学习方法为H. pylori感染分类提供了一个有希望的,自动化的方法.
- 这种人工智能工具有可能通过分析内镜图像来显著帮助胃癌查.
- 这种方法可以减少医生的工作量,提高诊断准确度.
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