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使用机器学习对Proteus mirabilis的抗生素配置分类:对多维放射学特征的调查

Amir Khorasani¹, Sharareh Moghim², Jeroen Wagemans³

¹Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Bioimaging, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Computers in biology and medicine

|September 11, 2024

概括

机器学习使用显微镜图像对Proteus mirabilis的抗生素耐药性进行了分类. 这种快速的方法有助于区分耐药菌株,打击抗菌素耐药性 (AMR) 的医疗威胁.

科学领域:

微生物学微生物学
医疗成像医学成像
计算生物学计算生物学

背景情况:

抗菌素耐药性 (AMR) 是一个主要的全球健康问题.
导管相关性尿路感染 (CAUTIs) 的原因之一的Proteus mirabilis,显示出抗生素耐药性日益增加.
目前以培养为基础的细菌耐药性诊断是耗时的.

研究的目的:

开发一种快速且具有成本效益的方法来分类Proteus mirabilis的抗生素耐药性概况.
探索机器学习 (ML) 与微观成像和放射学相结合的应用,用于电阻分类.
为此分类任务评估不同ML模型的性能.

主要方法:

从CAUTI患者中收集了150个P.奇迹菌株.
使用基于波形和常规显微镜的成像与预处理 (消噪,正常化,面具创建).
提取和选择的放射学特征用于训练和测试具有5倍交叉验证的ML模型.

主要成果:

30%的菌株表现出多药耐药性,60%表现出强烈的生物膜活性.
随机森林 (RF) 算法获得了高性能:ACC=0.95,特异性=0.97,灵敏度=0.88,AUC=0.98.
使用射频进行基于波纹的放射学分析,证明了电阻分类的巨大潜力.

结论:

显微镜成像与放射学和ML相结合,为传统方法提供了精确和快速的替代方案.
这种方法有助于区分抗生素耐药的P. mirabilis菌株.
开发的方法代表了通过更快的诊断来打击AMR的重大进步.

关键词:

抗生素的特征抗生素的特征机器学习机器学习奇迹性的P.奇迹性的无线电学 (Radiomics) 是一种辐射学.随机的森林随机的森林

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