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一个机器学习辅助的Ag-TiO₂ SERS平台用于手术内骨髓炎诊断

Yunfan Chen¹, Tengjiao Zhu^2,3, Langran Wang^2,3

¹School of Materials Science and Technology, China University of Geosciences, Beijing 100083, China. lzhao@cugb.edu.cn.

|March 5, 2026

概括

这项研究介绍了一种基于Ag-TiO2的新型表面增强拉曼散射 (SERS) 平台,用于快速诊断骨髓炎. 先进的SERS技术与人工智能结合,可以从最小的液体样本准确地识别骨感染.

科学领域:

生物医学工程生物医学工程
分析化学分析化学
传染性疾病传染性疾病

背景情况:

骨髓炎的诊断面临挑战,因为目前的方法的灵敏度低,特异性低,结果延迟.
骨髓炎的手术内评估特别困难,影响了手术精度和患者的结果.
现有的诊断工具,如细菌培养和成像,不足以进行快速,准确的评估.

研究的目的:

开发一个快速的,无标签的骨髓炎诊断平台,使用表面增强的拉曼散射 (SERS).
评估SERS与人工智能 (AI) 结合用于手术内骨髓炎检测的临床可行性.
调查Ag-TiO2 SERS平台中信号增强的潜在机制.

主要方法:

开发一种用于SERS分析的二氧化银- (Ag-TiO2) 纳米复合基质.
使用SERS平台在伤口盐水灌液 (WSIF) 中检测骨质炎生物标志物.
应用人工智能 (AI) 模型来分类感染者,非感染者和康复患者的拉曼光谱.

主要成果:

Ag-TiO2 SERS平台展示了临床样本中生物化学变化的超敏感和可重复检测.
电磁场放大和电荷转移相互作用的协同效应显著增强了拉曼信号强度.
人工智能模型成功地分类了不同的拉曼指纹,识别了与细菌感染和免疫反应相关的代谢变化.

结论:

集成的SERS-AI平台为快速,无标签的骨髓炎手术内诊断提供了一个有希望的策略.
这种方法可以提高外科手术的精度,降低复发率,并提高患者对骨感染的管理.
该研究阐明了SERS增强的机制基础,为先进的诊断工具铺平了道路.

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