人工智能合自校准SERS光谱用于对糖尿病及其相关并发症进行稳健的临床诊断
Kaitao Lai1, Shoubin Long2, Shah Faisal1
1Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Analytical chemistry
|November 7, 2025
概括
这项研究介绍了一种使用金纳米粒子和人工智能用于早期糖尿病检测的先进诊断模型. 人工智能辅助的SERS技术在最小的样本中提供了高精度,改善了患者的治疗结果.
科学领域:
- 生物医学工程 生物医学工程
- 纳米技术纳米技术
- 人工智能的人工智能
背景情况:
- 糖尿病 (DM) 存在诊断方面的挑战,特别是早期并发症.
- 传统的诊断方法缺乏早期检测的灵敏度和准确性.
- 准确的早期诊断对于有效的治疗和降低成本至关重要.
研究的目的:
- 开发用于早期糖尿病和并发症检测的先进诊断模型.
- 将人工智能 (AI) 与自校准的表面增强拉曼光谱 (SERS) 系统集成.
- 克服传统诊断技术在灵敏度和范围上的局限性.
主要方法:
- 使用的黄金纳米粒子 (AuNPs) 与4-mercaptophenylboronic acid (AuNPs@4MPBA) 功能化,用于SERS检测.
- 与SERS诊断系统集成了一种人工智能辅助的自我校准方法.
- 采用ResNet-LSTM多头自我注意神经网络进行分类和检测.
主要成果:
- 综合SERS-AI方法使用最小的样本体积证明了高的诊断准确性.
- 该模型允许精确地分类和检测糖尿病和相关并发症.
- 该诊断方法准确地确定了糖尿病患者,即使有前期药物治疗史,也超过了传统方法.
结论:
- 开发的自校准SERS-AI诊断方法为早期糖尿病检测提供了灵敏而准确的方法.
- 这种人工智能集成系统克服了传统方法的局限性,包括跟踪药物后并发症.
- 该模型的概括性和准确性通过共弦相似性和皮尔森相关性得到验证,减少了临床误诊.
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