在基于深度学习的虚拟红色红色染色内镜中评估早期胃癌的可见性 (带视频)
Ayaka Takasu1, Sho Suzuki2, Yusuke Monno3
1Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Koto-ku, Japan.
Endoscopy international open
|January 22, 2026
概括
一个深度学习系统从白光内镜 (WLE) 中生成了虚拟的色内镜 (IC) 图像. 虚拟IC在近一半的评估中改善了早期胃癌 (EGC) 的可见性,为传统IC提供了潜在的替代方案.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 红色染色内镜 (IC) 有助于早期胃癌 (EGC) 诊断,但复杂且耗时.
- 白光内镜 (WLE) 是标准的,但其EGC检测可能是有限的.
- 深度学习有可能增强内镜成像.
研究的目的:
- 开发和评估一个深度学习系统,用于从WLE生成虚拟IC图像.
- 评估EGC在基于视频的虚拟IC中的可见性,与WLE和真实IC相比.
主要方法:
- 一个循环一致的生成对抗网络 (CycleGAN) 在4096张内镜图像 (WLE和真实IC) 上受过训练.
- 该模型从WLE视频中生成了五个EGC案件的虚拟IC视频.
- 十六位内镜师使用7分尺度评估了WLE,真实IC和虚拟IC视频中EGC的可见性.
主要成果:
- 真实IC的中位可见度得分为1 [0-2],而虚拟IC得分为0 [-1至1] (P <0.001).
- 虚拟IC在46.3%的评估中获得了+1或更高的分数.
- 可见度得分因所使用的内镜系统而异 (P < 0.001).
结论:
- 通过深度学习生成的虚拟IC,在很大一部分情况下,与WLE相比,改善了EGC的可见性.
- 虽然与真实IC有效性不匹配,但虚拟IC显示出作为实际替代品的希望.
- 为特定的内镜平台优化该系统可能会提高其用于EGC检测的临床实用性.
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