使用基于图像的人工智能进行静脉极集成自动化尿路状态监测技术:一项模拟研究
Min Jae Kim1, Gun Ho Kim2, Subrata Bhattacharjee1
1Medical Research Institute, Pusan National University, Yangsan, Korea.
Biomedical engineering letters
|January 26, 2026
概括
一个新的IV杆系统自动监测尿管患者的尿液,检测颜色和体积变化以识别尿路疾病症状. 这项技术有可能提高患者的安全性和实时健康状况跟踪.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 导管患者的长期监测需要工具来跟踪尿液输出和特征.
- 目前用于监测尿液颜色和体积的方法通常是人工和劳动密集型的.
- 需要自动化系统来提高尿管护理的效率和患者安全.
研究的目的:
- 开发和验证一个自动化系统,用于监测尿液颜色和空洞模式在导管患者.
- 评估基于深度学习的技术的准确性,用于检测尿路疾病症状和估计尿量.
- 评估静脉注射 (IV) 极集成系统对实时患者监测的潜力.
主要方法:
- 一种新的排尿状态监测技术被整合到一个IV极.
- 深度学习被用来检测尿袋中的液体颜色和体积.
- 一项概念验证模拟研究使用了各种模拟的尿样来测试系统的性能.
- 为了评估准确性和可靠性,进行了超过24小时的长期测试.
主要成果:
- 该系统在预测不同尿液状况的囊中液体体积时实现了较低的误差率 (例如,紫色尿囊综合征的2.00±4.93%).
- 袋子冲洗请求报警的错误率在0.71-1.08%之间.
- 在24小时的测试中,该系统准确地对尿路疾病症状进行了分类 (100%的准确度),并估计了根据病情的差异错误率 (例如,8.75±4.61%为小尿症) 的空虚体积.
结论:
- 拟议的IV极集成尿道监测技术显示,对实时,简化监测导管患者有希望.
- 这项技术有可能提高患者的安全性,特别是对于患有脏和泌尿病的患者.
- 需要对实际尿样进行进一步的临床评估,以确认这些发现.
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