多模式内镜人工智能系统改善结直肠亚皮质病变的分类:一个多中心研究
Shurong Chen1, Louzhe Xu2, Yihong Zhang1
1Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
概括
一个新的AI系统,AIOSCOPE-WE,使用多模式内镜准确分类结直肠亚皮质病变 (SELs). 这种工具显著提高了内镜诊断的性能,以获得更好的临床决策.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 结肠直肠亚皮质病变 (SELs) 呈现出不同的组织学和预后,尽管具有相似的内镜外观,这给诊断带来了挑战.
- 准确的SEL分类对于确定适当的患者管理和治疗策略至关重要.
研究的目的:
- 开发和评估一个名为AIOSCOPE-WE的多模式内镜人工智能 (AI) 系统,用于分类结直肠SELs.
- 将人工智能系统的诊断性能与单模人工智能模型和人类内镜师进行比较.
主要方法:
- 开发单模 (WLE,EUS) 和多模 (AIOSCOPE-WE) AI模型,使用510名确诊结直肠SEL患者的回顾性数据.
- 内部和外部验证人工智能模型的分类性能.
- 用AIOSCOPE-WE辅助和不使用AIOSCOPE-WE辅助的内镜师诊断准确性的比较.
主要成果:
- 与EUS专家 (75.8%) 和非专家 (65.4%) 相比,AIOSCOPE-WE在分类结直肠SEL方面表现出更高的准确性 (86.0%).
- 在AIOSCOPE-WE支持下,内镜师的精度从70.6%显著提高到83.1%.
- 在内部 (92.1%) 和外部 (84.9%) 测试中,AIOSCOPE-WE的表现优于单模模型 (WLE,EUS),在各种损伤特征中表现一致.
结论:
- AIOSCOPE-WE系统显示出作为内镜师在诊断结直肠SEL中的有效工具的巨大潜力.
- 这种人工智能驱动的方法可以提高诊断准确度,并优化结直肠SEL患者的临床决策.
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