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基于CNN的诊断模型,使用单个肠道压力信号来测试儿童的膀符合性

Gang Yuan^1,2, Zicong Ge^1,2, Jian Zheng^1,2

¹School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.

Computer methods in biomechanics and biomedical engineering

|January 9, 2024

概括

一个新的1D-CNN模型有效地选膀遵守,有助于诊断膀疾病. 这种方法为传统的泌尿动力学研究提供了更快的替代方案,特别适用于儿童.

科学领域:

泌尿器科泌尿器科泌尿器科泌尿器科
生物医学工程生物医学工程
人工智能在医学中的应用

背景情况:

膀合规性评估对于诊断膀功能障碍至关重要.
尿动力学研究 (UDS) 是主要的方法,但复杂且耗时,特别是对于儿科患者.
需要一种有效的UDS前查方法来确定膀是否符合要求.

研究的目的:

开发和验证1D-CNN模型,用于准确和高效的膀合规性查.
在UDS之前创建一个更快,更容易使用的方法来识别潜在的膀功能障碍.

主要方法:

使用尿动力学研究 (UDS) 数据构建了一个数据集.
一个1D卷积神经网络 (1D-CNN) 模型被设计,优化和训练.
经过训练的模型应用于来自新型 perfusion 实验的数据.

主要成果:

与其他评估的算法相比,1D-CNN模型显示出更高的性能.
该模型准确有效地识别了异常的膀符合性.
输液实验数据,当通过模型分析时,产生了有希望的结果.

结论:

开发的1D-CNN模型显示出作为膀合规性的准确和高效查工具的巨大潜力.
这种方法可以简化膀功能障碍的诊断过程,减少在初始评估中依赖耗时的UDS.
需要在临床环境中进一步验证,以确认其在儿科泌尿病学中的有用性.

关键词:

膀符合规定的卷积神经网络是一种卷积神经网络.静脉内压力静脉内压力尿动力学研究尿动力学研究

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