通过智能医疗卫生所进行人工智能驱动的排便分析:探索生物识别模式和Eu-Tenesmus
Zhiquan Song1, TaeHyung Kwon2, Jeung Lee2
1School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore.
概括
一个新的智能所系统监测排便,识别关键参数,如便的持续时间和厚度. 这项技术为个性化健康跟踪和非侵入性生物识别提供了潜力.
科学领域:
- 生理学监测是指对身体进行生理监测.
- 生物识别识别生物识别
- 肠道健康评估 肠道健康评估
背景情况:
- 排便是一个至关重要的生理过程,在健康监测中经常被忽视.
- 目前评估排便的方法有限,缺乏实时的客观数据.
- 需要先进的工具来全面了解和跟踪肠道功能.
研究的目的:
- 开发和验证智能所系统,以实时监测排便行为.
- 识别和分析关键的排便参数,以客观地评估肠道健康.
- 探索智能所数据在个性化健康跟踪和生物识别方面的潜力.
主要方法:
- 开发一个智能所系统,能够实时监控排便.
- 分析了来自11名参与者的45次排便事件,以确定关键参数.
- 统计分析以将排便参数与便一致性 (布里斯托尔便形式尺度) 和人口统计数据相关联.
主要成果:
- 确定了关键参数:便掉落的持续时间,便厚度和eu-tenesmus间隔.
- 较长的便时间与较低的布里斯托尔便表格评分相关 (便秘).
- 便的厚度与布里斯托尔便形状量表得分有显著的反向相关性.
- 引入了"eu-tenesmus"作为肠道健康的潜在独立生物标志物,具有显著的性别差异.
- 使用排便相关参数证明生物识别的可行性.
结论:
- 智能所系统提供客观的,实时的关于排便行为的数据.
- 排便的参数,如便的厚度和持续时间,可以作为肠道功能的客观标记.
- 欧特尼斯显示出作为肠道健康的新生物标志物的潜力.
- 智能所技术有望实现个性化的健康监测,饮食建议和非侵入性身份验证.
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