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结肠囊内镜中的多匹配:开创CCE-结肠镜集成,朝着人工智能驱动的未来迈进

Ian Io Lei^1,2, Ramesh Arasaradnam^1,2,3,4, Anastasios Koulaouzidis^5,6,7,8

¹Institute of Precision Diagnostics & Translational Medicine, University Hospital of Coventry and Warwickshire, Clifford Bridge Rd, Coventry CV2 2DX, UK.

Journal of clinical medicine

|December 17, 2024

概括

在结肠囊内镜 (CCE) 中精确的聚匹配对于其成本效益至关重要. 目前的方法难以报告重复的息肉,但机器学习显示了提高CCE息肉检测准确度的希望.

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:

结肠囊内镜 (CCE) 的采用受到后续结肠镜检查的需要所阻碍.
由于囊运动,在CCE中报告重复的息肉会影响成本效益.
精确的聚匹配对于将CCE发现与结肠镜检查程序相结合至关重要.

研究的目的:

审查聚匹配技术和CCE中的准确性.
评估机器学习 (ML) 模型,以改善多匹配.
评估CCE聚匹配的挑战和未来方向.

主要方法:

在EMBASE,MEDLINE和PubMed中进行系统的文献搜索.
包括临床试验,观察性研究,综述和病例系列.
对三项直接相关的研究和十项间接相关的研究进行了审查.

主要成果:

在CCE中,聚匹配是不发达的,视频内匹配标准有限.
经验丰富的CCE读者表现出更高的准确性和较低的观察者间变异性.
一个ML算法在CCE和结肠镜之间实现了大约50%的聚匹配精度,需要优化.
在CCE聚检测中推进AI可能会增加重复报告的风险.

结论:

在CCE中,聚匹配带来了重大挑战,特别是在单个视频中.
人工智能证明了提高CCE中聚匹配精度的潜力.
需要进一步的研究来完善人工智能技术,以便可靠的CCE聚匹配和与结肠镜整合.

关键词:

囊内镜检查囊内镜检查结肠癌是什么意思结肠癌是什么意思结肠囊内镜结肠囊内镜结肠镜检查结肠镜检查结肠直肠癌是什么意思聚合物 (polyp) 是一种多重体.

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