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AssiST:用于分析抗生素敏感性测试的卷积神经网络

Carmen Li¹, Sydney Schock¹, Abigail Costa¹

¹Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01655, United States.

Bioinformatics advances

|March 4, 2026

概括

AssiST是一个新的卷积神经网络 (CNN) 管道,分析扫描微稀释板的微生物生长,以确定抗生素易感性. 这种可扩展的工具使用标准计算机提供可重现的药物敏感性结果.

科学领域:

微生物学微生物学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

抗生素敏感性测试 (AST) 对于指导抗菌疗法至关重要.
当前的AST方法可能是劳动密集型和耗时的.
需要可扩展和可重复的方法来确定药物敏感性.

研究的目的:

引入AssiST,一个卷积神经网络 (CNN) 管道用于自动化抗生素敏感性测试.
为了从扫描图像中分类微生物生长在96井微稀释板中的微生物生长.
为推断药物易感性提供灵活和可扩展的解决方案.

主要方法:

基于图像的微生物生长分类CNN管道 (AssiST) 的开发.
使用扫描图像的96井微稀释板.
实施用户可配置的表型对敏感性调用的映射.

主要成果:

AssiST准确地分类了微生物生长模式,表明药物易感性.
该管道容纳了各种微生物形态和实验条件.
AssiST能够将扫描仪图像转换为可重现的药物敏感度读数.

结论:

AssiST提供了一种可扩展,自动化和可重复的抗生素敏感性测试方法.
该管道支持在各种微生物,介质和药物中灵活应用.
AssiST使实验室能够使用标准硬件高效地生成药物敏感性数据.

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