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供应商不可知视觉转换器基于人工智能用于腹腔胆管镜检查:与卷积神经网络和内镜检查相比,胆管狭窄的诊断性能

Ryosuke Sato¹, Kazuyuki Matsumoto¹, Masahiro Tomiya²

¹Department of Gastroenterology and Hepatology, Okayama University Hospital, Okayama, Japan.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society

|September 4, 2025

概括

使用视觉转换器 (ViT) 架构的新人工智能 (AI) 系统通过口腔胆管镜 (POCS) 改善了胆管狭窄的诊断. 这种人工智能显示的准确性与人类内镜医生相美.

科学领域:

医学成像
人工智能
胃肠病学

背景情况:

精确诊断胆道狭窄是非常重要的, 但具有挑战性.
口腔胆道透视 (POCS) 是用于胆道狭窄评估的内镜程序.
现有的诊断方法在准确性和一致性方面可能存在局限性.

研究的目的:

使用POCS图像开发和评估用于诊断胆管狭窄的人工智能系统.
评估视觉变压器 (ViT) 架构对此诊断任务的性能.
将人工智能系统的准确性与传统方法和人类内镜师进行比较.

主要方法:

对经过POCS的125名胆管狭窄的患者进行了回顾性分析.
开发人工智能模型,包括视觉转换器 (ViT) 和卷积神经网络 (CNN),使用来自不同供应商系统的POCS图像.
一个盲目的评估,比较ViT模型的性能与专家和非专家内镜师使用专门的测试套件.

主要成果:

基于ViT的AI模型在整体数据集上实现了83.9%的诊断准确性.
不同的POCS供应商系统 (CHF和Spy组) 的性能是一致的.
ViT模型的准确性 (78.4%) 与测试组中的专家内镜 (82.0%) 和非专家 (73.0%) 相比.

结论:

一种基于视觉转换器 (ViT) 的新型人工智能模型通过POCS证明了胆道狭窄的高诊断准确性.
人工智能系统不依赖供应商, 性能与传统的CNN和人类内镜相比.
这种人工智能工具有可能提高胆管狭窄诊断的准确性和效率.

关键词:

人工智能胆道新生体胆血管镜检查计算机辅助诊断视觉变压器

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