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人工智能增强升级转换纳米粒子基于横向流量试验通过转移学习.

Wei Wang1, Kuo Chen2, Xing Ma3

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Fundamental research
|June 27, 2024
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概括

人工智能 (AI) 增强了基于纳米颗粒的向上转换侧流测试 (UCNP-LFA) 进行精确的护理点测试 (POCT). 这种AI驱动的UCNP-LFA策略克服了在各种环境中可靠的实时定量检测数据的局限性.

关键词:
互联网的医疗东西的互联网.侧向流量检测试验 侧向流量检测试验可携带的光传感器传感器转移学习转移学习向上转换纳米粒子

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科学领域:

  • 纳米技术和生物传感技术
  • 计算机视觉中的人工智能
  • 护理点诊断的诊断方法

背景情况:

  • 上转化纳米粒子 (UCNPs) 与免疫染色学相结合,提供了有前途的治疗点测试 (POCT),但面临着低发光效率和图像噪声等挑战.
  • 人工智能 (AI) 显示了计算机视觉在改进数据分析和克服当前检测技术的局限性方面的巨大潜力.

研究的目的:

  • 通过将人工智能与基于上转换纳米粒子的横向流量测试 (UCNP-LFA) 集成,开发一种新的定量检测策略.
  • 提高UCNP-LFA的准确性,稳定性和适用性,用于实时定量检测,特别是在现场环境中.
  • 为了解决POCT设备中的数据稀缺性和低计算能力限制.

主要方法:

  • 利用转移学习来训练AI模型在一个小型的,自建数据库上进行UCNP-LFA定量检测.
  • 在物联网 (IoT) 设备中部署训练有素的人工智能模型,以便在没有大量数据预处理的情况下实时推断.
  • 通过使用八种转移学习模型对两个探测器进行定量检测,包括添加噪声的测试,验证了战略.

主要成果:

  • 在使用人工智能训练的UCNP-LFA进行定量检测的准确性和稳定性方面取得了显著的改进.
  • 从人工智能模型中证明了超高准确度的预测结果 (高达100%),即使在强大的噪音条件下.
  • 展示了该战略在现场检测环境中的适用性及其克服POCT设备局限性的能力.

结论:

  • 集成AI的UCNP-LFA战略提供了一个适合光学生物传感器的通用,准确和敏感的定量检测方法.
  • 这种方法有效地解决了POCT的实际挑战,为该领域的革命性进展铺平了道路.
  • 开发的设备在体外诊断 (IVD) 产业中具有显著的科学价值,可以改变POCT技术和商业潜力.