基于内视图的人工智能预警系统用于结肠镜输管检查.
Yigeng Huang1,2, Suwen Li3,4, Syeda Sadia Rubab1,2
1State Key Laboratory of Transducer Technology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
Scientific reports
|April 28, 2025
概括
人工智能 (AI) 现在可以量化结肠镜红外视图,这是一个常见的问题,可能导致穿孔. 这种人工智能系统有效地评估了避开红外的技能,有助于更安全,更有效的结肠镜训练.
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:
- 结肠镜输管可以导致粘膜损伤,可视化为"红色"视图,增加穿孔风险.
- 目前还缺乏用于评估结肠镜检查期间红色视图的定量方法.
- 开发客观的评估工具对于提高结肠镜安全性和培训至关重要.
研究的目的:
- 开发和验证基于人工智能 (AI) 的系统,用于定量结肠镜输管过程中红色视图.
- 评估人工智能衍生的红外避免得分和内镜师经验水平之间的相关性.
- 评估人工智能系统作为结肠镜医生的培训工具的实用性.
主要方法:
- 一个半监督的人工智能系统被开发出来,分析了34名结肠镜师的479次结肠镜检查.
- 基于人工智能的避免红外分数在新手,中级和经验丰富的内镜组之间进行了比较.
- 评分还与专家评级的基于直接观察程序或技能 (DOPS) 的小费控制评估进行了比较.
主要成果:
- 在经验水平 (p < 0.001) 之间观察到实际的红外视图百分比和基于AI的红外避免得分的显著差异.
- 具有较高DOPS尖端控制分数的内镜师表现出更好的基于AI的红外避免.
- 人工智能避开红外的得分与腔输管时间和实际红外百分比有负相关性.
结论:
- 基于人工智能的系统提供了在结肠镜检查期间避免红外的定量测量.
- 这种人工智能工具可以区分技能水平,并与已建立的评估方法相关联.
- 来自人工智能系统的反可以增强结肠镜培训,促进更安全,更有效的程序.
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