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使用新型智能所进行长期的自动化便监测:可行性研究

Jin Zhou¹, Yuying Luo², Julia W Darcy¹

¹Duke University, Durham, North Carolina, USA.

Neurogastroenterology and motility

|November 1, 2024

概括

一个新的智能所系统使用计算机视觉自动监测便形状,证明了长期数据收集和精确评估肠道运动一致性的可行性.

科学领域:

胃肠病学胃肠病学
医疗技术医疗技术医学技术
计算机视觉计算机视觉

背景情况:

患者报告的肠道排便一致性往往是不可靠的.
自动化便分析提供了更客观的评估方法.
布里斯托尔便形状量表 (Bristol Stool Form Scale,简称BSFS) 是一种用于分类便一致性的标准.

研究的目的:

用智能所来证明长期便图像数据收集的可行性.
通过BSFS评估计算机视觉分析方法来评估便形式.
开发一种非侵入性方法来监测肠道运动的一致性.

主要方法:

一个配备成像门户的新型智能所被开发并安装在工作场所.
六名健康的志愿者在10个月的时间里提供了474张便图像.
计算机视觉算法,结合深度学习和手工制作的功能,被用来分析便形态.

主要成果:

该系统成功地在长时间内收集了便图像.
异常便形式 (BSFS 2和6) 分别在3%和4%的图像中被发现.
图像分析算法在分类异常便形式时实现了94%的准确性,81%的灵敏性和95%的特异性.

结论:

智能所系统是一个可行的和准确的工具,用于非侵入性,长期的便形式监测.
自动化便分析可以消除与手动跟踪相关的患者负担.
这项技术有可能改善对胃肠道健康的客观评估.

关键词:

人工智能的人工智能是人工智能.计算机视觉计算机视觉便秘便秘便秘便秘腹腹腹腹腹腹远程监控患者的患者监控.

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