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通过自我转移深度学习和基于单细胞拉曼光谱的集体预测来智能识别食源性致病细菌

Daixi Li¹, Yuqi Zhu², Aamir Mehmood³

¹Institute of Biothermal Engineering, University of Shanghai for Science and Technology, Shanghai, 20093, China; Peng Cheng National Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China.

|December 7, 2024

概括

这项研究介绍了一种使用拉曼光谱的AI模型,以准确检测食物传播细菌. 这种先进的模型达到99.99%以上的准确性,改善了疾病诊断和减少了虚假阴性.

科学领域:

微生物学微生物学
频谱学是一种光谱学.
人工智能的人工智能

背景情况:

通过食物传播的致病性感染是全球主要的健康问题.
准确和快速检测食源性病原体对于预防疾病传播和确保食品安全至关重要.
目前的检测方法可能面临速度,准确性或全面分类的限制.

研究的目的:

开发和验证一种人工智能 (AI) 模型,用于精确识别食源性致病细菌.
利用单细胞拉曼光谱法 (SCRS) 结合先进的机器学习来增强细菌检测.
在多个分类级别中实现高精度,包括Gram状态,属,物种和菌株.

主要方法:

使用单细胞拉曼光谱 (SCRS) 来从单个细菌细胞中获取光谱数据.
开发了一个人工智能模型,结合了自我转移深度学习和整体预测算法.
训练并验证了该模型在各种食物传播的致病细菌上,评估四个分类级别的性能.

主要成果:

人工智能模型实现了超过99.99%的异常准确度,用于识别格拉姆阴性和阳性细菌,属和物种.
据报道,菌株水平识别准确率超过99.49%.
该模型在所有四个分类层都展示了前所未有的预测性能,显著超过现有方法.

结论:

开发的AI模型提供了一种高度准确和高效的方法,用于使用SCRS识别食源性致病细菌.
这一进步对改善医疗检测,诊断和食品传播疾病的整体管理具有重大意义.
该模型的高精度和减少假阴性结果的能力有助于提高食品安全和公共卫生保护.

关键词:

人工智能的人工智能是人工智能.整体预测的预测食物传播的致病性细菌是通过食物传播的.自己转移深度学习单细胞拉曼光谱学

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