基于的尿路结石的元素组成分析通过激光诱导分解光谱来增强临床洞察力
Haijie Xie1, Junkai Huang1, Rui Wang2
1Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, No. 23 Pingjiang Road, Hexi District, Tianjin, 300211, People's Republic of China.
Scientific reports
|September 29, 2025
概括
激光诱导分解光谱 (LIBS) 分析了基于的尿路结石中的元素组成. 将LIBS数据与临床信息相结合,可以准确预测石头的复发情况,有助于尿病的管理.
科学领域:
- 地质化学 地质化学
- 分析化学 分析化学
- 生物医学工程 生物医学工程
背景情况:
- 尿路结石,特别是基于的类型,对健康构成重大挑战.
- 了解元素组成对于识别岩石形成机制和复发因素至关重要.
- 当前的诊断方法可能无法完全捕捉与复发相关的石头形状的复杂性.
研究的目的:
- 用激光诱导分解光谱学 (LIBS) 来描述基于的尿路结石的元素组成.
- 开发一个机器学习模型,整合基本和临床数据,以区分与复发相关的石头形状.
- 评估模型的性能和可通用性,以便在尿病管理中潜在的临床应用.
主要方法:
- 分析了122个基于的尿路结石,使用LIBS来确定元素强度比.
- 追溯收集临床变量,包括人口统计,实验室结果和复发状态.
- 使用各种数据整合策略 (仅临床,光谱,组合,合并) 开发和评估反向传播神经网络 (BPNN) 模型.
- 外部验证使用来自不同医疗中心的16个石头样本.
主要成果:
- 在大多数基于的石头中检测到 (Mg) 和 (Sr).
- 在元素比率 (P/Ca,Mg/Ca,Sr/Ca,K/Ca) 之间观察到显著的相关性.
- 经常形成石头的人表现出高的元素比率,更高的尿液pH值和更低的石头CT密度.
- 结合合并的光谱和临床数据的BPNN模型在分类复发相关的个人资料方面取得了最高的准确性 (94.37%),超过仅临床模型 (73.37%).
- 外部验证证实了该模型的良好概括能力.
结论:
- LIBS有效地揭示了基石中的Mg和Sr的存在,并在反复发生的情况下识别出高元素比率.
- 通过LIBS获得的元素配置文件与临床数据相结合,可以高精度地对复发相关的石头配置文件进行分类.
- 这种方法为潜在的风险分层和尿病的个性化管理策略提供了宝贵的见解.
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