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机器学习辅助色度传感器阵列,用于智能和快速诊断尿路感染

Jianyu Yang¹, Ge Li¹, Shihong Chen²

¹School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.

|March 26, 2024

概括

一个新的机器学习传感器阵列快速识别尿路感染 (UTI) 病原体. 这一突破为常见的细菌感染提供了快速,准确的诊断工具,改善了患者的治疗结果.

科学领域:

生物医学工程生物医学工程
纳米技术纳米技术
传染性疾病传染性疾病

背景情况:

尿路感染 (UTI) 在全球普遍存在,导致严重的并发症和由于抗菌素耐药性增加而增加的医疗费用.
目前的尿路感染诊断,如尿液培养,是缓慢的,需要专门的设备,阻碍及时治疗.
耐药性病原体的出现需要先进的诊断解决方案,以有效管理尿路感染.

研究的目的:

开发一个快速,准确和具有成本效益的诊断平台,用于识别导致尿路感染的微生物.
为了利用机器学习和基于纳米酶的色度传感器进行微生物识别.
为了能够精确地检测各种分类层次 (序列,属,物种) 的细菌.

主要方法:

使用连接体功能化的铁单原子纳米酶 (Fe SANs) 制造色度传感器阵列.
用四种不同的识别配体对Fe SANs进行功能化,以创建独特的微生物指纹.
将传感器阵列与用于自动分析的机器学习分类模型集成.

主要成果:

传感器阵列成功识别了在尿道感染样本中常见的10多种不同的微生物.
该诊断平台实现了高精度,在临床尿道感染样本中达到高达97%.
在一小时内完成了微生物识别,大大缩短了诊断时间.

结论:

开发的机器学习辅助色度传感器阵列为UTI诊断提供了快速而准确的方法.
这项技术显示出临床应用的巨大潜力,解决了当前诊断方法的局限性.
该平台快速识别病原体的能力可以帮助及时和适当地治疗尿路感染.

关键词:

铁单原子纳米酶是一种Fe单原子纳米酶.颜色测量传感器阵列的颜色测量.机器学习是机器学习.微生物的识别方法尿路感染尿路感染

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