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快速和敏感的急性白血病分类和诊断平台使用深度学习辅助的SERS检测.

Dongjie Zhang1, Zhaoyang Cheng2, Yali Song3

  • 1Center for Biomedical-photonics and Molecular Imaging, Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Bi-optoelectronic-integration and Medical Instrumentation Laboratory, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong 510555, China; State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipment, Xidian University, Xi'an, Shaanxi 710071, China.

Cell reports. Medicine
|September 9, 2025
PubMed
概括

这项研究引入了一种快速深度学习和表面增强的拉曼散射 (DL-SERS) 方法,用于使用脑脊液诊断急性白血病 (AL). DL-SERS方法提供了灵敏和准确的检测,有助于早期疾病的识别.

关键词:
患有急性白血病的人.大脑脊髓液,大脑脊髓液深度学习是一种深度学习.表面增强的拉曼散射

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科学领域:

  • 生物医学诊断 生物医学诊断
  • 计算生物学 计算生物学
  • 频谱学是一种光谱学.

背景情况:

  • 准确和快速诊断急性白血病 (AL) 对于有效的患者管理至关重要.
  • 当前的诊断方法可能需要大量的时间和样本.
  • 在AL的亚型和遗传异常识别对于治疗分层是必不可少的.

研究的目的:

  • 开发一种结合深度学习和表面增强的拉曼散射 (DL-SERS) 策略,用于快速和灵敏地识别急性白血病.
  • 评估DL-SERS方法在分类AL亚型和遗传异常方面的性能.
  • 探索DL-SERS平台用于诊断其他神经疾病的多功能性.

主要方法:

  • 收集和分析超过390个脑脊液 (CSF) 样本,包括健康对照组,AL患者和其他疾病组.
  • 使用表面增强的拉曼散射 (SERS) 以在5分钟内使用最小的CSF体积 (0.5μL) 进行敏感检测.
  • 实施综合特征融合方法 (1D光谱和2D图像) 用变压器模型进行分类.

主要成果:

  • 使用DL-SERS实现了急性白血病的快速和敏感检测.
  • 在AL诊断的准确性,敏感性,特异性和可靠性方面表现出卓越的分类性能.
  • 通过将其应用扩展到脑膜炎疾病的分类来展示该平台的多功能性.

结论:

  • 开发的DL-SERS分类策略为急性白血病的快速和敏感识别提供了一个强大的工具.
  • 这种方法为临床环境提供了潜在的辅助体外诊断工具.
  • 在疾病诊断中,DL-SERS平台具有更广泛应用的巨大潜力.