人工智能集成的光学生物传感器的新兴趋势,用于临床诊断:当前状态和未来前景
Sathishkumar Subburaj1,2, Conghui Liu1, Tailin Xu1
1Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong, 518060, China. conghuiliu@szu.edu.cn.
概括
人工智能 (AI) 增强了光学生物传感器,用于快速点诊断 (POCD). 这种整合提高了疾病检测的敏感性,特异性和未来医疗保健的实时决策.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 光学传感传感器是什么?
背景情况:
- 光学生物传感器提供敏感和快速的疾病检测,用于点诊断 (POCD).
- 将人工智能 (AI) 与光学生物传感器集成在POCD中承诺增强分析性能和实时决策.
研究的目的:
- 审查用于POCD的AI集成光学生物传感器的前景.
- 为突出传感器设计,数据科学和小型化方面的进步.
- 讨论人工智能算法在提高生物传感器性能方面的作用.
主要方法:
- 对应用到光学生物传感的AI算法 (机器学习,深度学习) 的审查.
- 分析AI对信号处理,模式识别和自动决策的影响.
- 通过人工智能增强的各种光学生物传感技术 (SPR,光,色度,拉曼) 的检查.
主要成果:
- 人工智能显著提高了光学生物传感器的灵敏度,特异性和复杂化能力.
- 人工智能有助于智能信号处理,模式识别和自动决策.
- 与物联网和云计算的整合为AI增强的光学生物传感器提供了新的可能性.
结论:
- 人工智能集成的光学生物传感器准备彻底改变POCD.
- 需要解决数据隐私,集成复杂性和临床验证方面的挑战.
- 本综述为研究人员,临床医生和行业利益相关者提供了未来前景.
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