在人类胃液中检测Helicobacter pylori感染通过表面增强的拉曼光谱学与机器学习算法相结合
Jia-Wei Tang1, Fen Li2, Xin Liu3
1Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China; School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China.
Laboratory investigation; a journal of technical methods and pathology
|December 22, 2023
概括
一种新的机器学习方法分析胃液SERS光谱,以快速检测Helicobacter pylori. 这种非侵入性技术显示出高准确性,有可能改善H. pylori的诊断.
科学领域:
- 生物医学工程 生物医学工程
- 医学诊断 医学诊断 医学诊断
- 机器学习 机器学习
背景情况:
- 目前的Helicobacter pylori诊断方法,如尿素呼吸测试和抗体测试,具有局限性,包括精度低和侵入性.
- 对于H. pylori感染而言,需要简单,快速和非侵入性的诊断工具.
研究的目的:
- 开发和评估一种新型的非侵入性方法,用于检测H. pylori感染,使用机器学习分析胃液表面增强的拉曼散射 (SERS) 光谱.
主要方法:
- 从100名参与者中采集了非侵入性胃液样本,使用弦测试.
- 使用机器学习算法生成和分析了12,000个SERS光谱,并根据其性能选择了光梯度增强机 (LightGBM).
- 该LightGBM模型在一组单独的2000个SERS光谱上得到验证,来自100名感染状态不明的参与者.
主要成果:
- 在初始模型构建中,LightGBM算法实现了99.54%的高预测准确度和2.61秒的时间效率.
- 在未知样本上对LightGBM模型进行盲测,与qPCR结果相比,预测准确率为82.15%.
结论:
- 从非侵入性收集的胃液中对SERS光谱的机器学习分析为H. pylori诊断提供了一个有希望的,简单的和快速的方法.
- 这种新技术有可能补充H. pylori感染的现有诊断方法.
相关概念视频
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