开发基于人工智能图像识别的侧流免疫染色学测试条,用于检测胺胺
Rui Zhang1, Yiyang Tong1, Yumeng Shi1
1Tianjin Key Laboratory of Food Quality and Health, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin, 300457, China.
Analytical biochemistry
|February 8, 2026
概括
这项研究引入了一种新的AI驱动的检测方法来检测运动中被禁止的物质胺 (HM). 该方法结合了侧流免疫检测与AI图像识别,用于快速准确的现场尿液检测.
科学领域:
- 分析化学 分析化学
- 生物技术是生物技术.
- 人工智能的人工智能
背景情况:
- 氨酸 (HM) 是体育运动中禁止使用的物质,需要快速检测方法.
- 目前的检测方法可能缺乏有效的反兴奋剂工作所需的速度和准确性.
- 人工智能 (AI) 提供了增强分析技术的潜力.
研究的目的:
- 开发和验证一种新的,AI集成的侧流免疫试验 (LFIA),用于尿液中快速检测HM.
- 建立使用基于人工智能的LFIA测试条的图像识别来检测HM的定量检测方法.
- 为现场反兴奋剂分析提供高效可靠的工具.
主要方法:
- 黄金纳米颗粒 (AuNPs) 与LFIA的HM特异性抗体的结合.
- 开发一种人工智能模型,用于对LFIA测试带颜色强度进行定量分析.
- 利用对比度有限的自适应直方体平衡 (CLAHE) 来改善AI模型训练中的特征提取.
- 为HM测试带图像分类创建数据集.
主要成果:
- 拟议的方法实现了HM的0.49 ng/mL的检测极限.
- 在2-8 ng/mL的度范围内,可以准确识别HM.
- 人工智能模型在尿样测试组上显示出96.88%的预测准确度.
结论:
- 人工智能增强的LFIA提供了一种高效可靠的解决方案,用于在现场快速检测HM.
- 这种方法解决了在反兴奋剂工作中及时监测的关键需求.
- 人工智能与LFIA的整合代表了体育反兴奋剂技术的重大进步.
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