使用高频超声波和卷积神经网络诊断糖尿病
Jeong Eun Lee1, Hyeon-Ju Jeon2, O-Joun Lee3
1Department of Biomedical Engineering, Pukyong National University, Busan 48513, Republic of Korea.
Ultrasonics
|September 27, 2023
概括
一个新的非侵入性糖尿病诊断使用高频超声波和AI. 这种方法分析红细胞的变化,为检测糖尿病提供了血液测试的有希望的替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
背景情况:
- 糖尿病发病率的增加需要改进诊断方法.
- 目前的糖尿病诊断技术存在局限性,包括准确度低和患者不适.
- 对于糖尿病管理而言,非常需要非侵入性诊断方法.
研究的目的:
- 开发和验证一种用于诊断糖尿病的新型非侵入性方法.
- 研究使用高频超声波 (HFU) 与卷积神经网络 (CNN) 结合用于糖尿病检测.
- 为了将由于葡萄糖度而导致的红细胞 (RBC) 特性变化与声信号特征相关联.
主要方法:
- 红血细胞的化与不同的葡萄糖度.
- 使用定制的90MHz传感器获取声学反射信号.
- 分析信号频谱和频谱图,使用CNN进行模式识别.
主要成果:
- 证明了HFU和CNN方法在糖尿病诊断中的有效性.
- 在识别红细胞中与葡萄糖相关的变化时,达到0.98的高分类准确度.
- 建立了RBC属性变化和可识别的信号特征之间的相关性.
结论:
- 开发的HFU和CNN方法显示了非侵入性糖尿病诊断的重大前景.
- 这项技术提供了一个潜在的体内诊断解决方案,不需要采集血液.
- 在开发方便和准确的糖尿病查工具方面取得了重大进展.
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