机器学习增强的SERS诊断:加速人工智能驱动的从实验室发现到临床实践的过渡
Biqing Chen1, Haizhu Sun1, Jiayin Gao1
1Gynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Heilongjiang, 150081, PR China.
Computers in biology and medicine
|June 3, 2025
概括
人工智能 (AI) 和机器学习 (ML) 增强了用于疾病诊断的表面增强拉曼光谱 (SERS). 这种整合自动化了数据分析,提高了检测癌症,感染和神经退行性疾病的准确性.
科学领域:
- 纳米技术和生物医学工程
- 计算科学和数据分析数据分析
背景情况:
- 表面增强拉曼光谱 (SERS) 为疾病诊断提供了敏感的分子指纹.
- 由于复杂的光谱数据,噪声和手动分析,SERS的临床应用受到限制.
- 人工智能 (AI) 和机器学习 (ML) 可以自动化SERS数据处理并提高诊断准确性.
研究的目的:
- 探索AI/ML和SERS在疾病检测方面的协同潜力.
- 为临床SERS应用突出先进的方法和可解释的AI.
- 审查癌症,传染病和神经退行性疾病的案例研究.
主要方法:
- 对AI/ML和SERS在诊断中的整合进行系统审查.
- 专注于自动化数据处理,特征提取和分类的方法.
- 包括可解释的人工智能工具和案例研究分析.
主要成果:
- 人工智能/ML-SERS集成在早期癌症检测和病原体识别方面表现出有效性.
- 这种方法对监测神经退行性疾病进展非常有希望.
- 挑战包括有限的数据集,模型可解释性和硬件-软件集成.
结论:
- 人工智能/ML-SERS集成对推进精确诊断具有重大前景.
- 未来的方向包括多模式学习,便携式设备和标准化验证.
- 这种跨学科的方法将纳米技术,生物医学和计算科学联系起来,以改善医疗保健结果.
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