基于唾液的诊断预测口腔状细胞癌使用FTIR光谱学
Priya Shree1, Yogendra Aggarwal2, Manish Kumar3
1Dental College, Rajendra Institute of Medical Sciences (RIMS), Bariatu, Ranchi, Jharkhand 834009 India.
概括
里埃变换红外光谱 (FTIR) 唾液显得有望用于口腔癌的检测. 这种非侵入性方法与机器学习相结合,在将口腔癌患者与健康个体区分开来时,获得了超过91%的准确性.
科学领域:
- 生物医学光谱学 生物医学光谱学
- 机器学习在诊断中的应用
- 口腔瘤学 在口腔瘤学
背景情况:
- 口腔癌是一个重要的全球健康问题,死亡率很高.
- 早期检测对于改善患者的治疗结果至关重要.
- 目前的诊断方法可能具有侵入性或缺乏敏感性.
研究的目的:
- 为了评估减弱总反射率-里埃变换红外光谱 (ATR-FTIR) 唾液查口腔癌的疗效.
- 开发一种机器学习模型,使用FTIR光谱数据从健康对照组分类口腔癌.
主要方法:
- 从30名口腔状细胞癌 (OSCC) 患者和30名健康对照人群中收集了唾液样本.
- 使用ATR-FTIR光谱分析了唾液的生物化学成分.
- 支持矢量机 (SVM) 分类用于根据光谱数据区分患者组.
主要成果:
- FTIR分析揭示了OSCC患者和健康个体之间唾液的明显光谱差异,特别是蛋白质二次结构的变化.
- 在区分口腔癌患者方面,SVM分类器实现了91.66%的整体准确性.
- 观察到高特异性 (100.0%) 和精度 (100.0%),灵敏度为83.33%.
结论:
- 唾液的ATR-FTIR光谱学与机器学习相结合,为口腔癌查提供了一种可靠,非侵入性和无试剂的方法.
- 这种方法显示出高准确性和早期检测和监测口腔癌的潜力.
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