在临床细菌学中开发和评估用于细菌生长监测的人工智能
Damien Jacot1, Shklqim Gizha1, Cedrick Orny2
1Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Journal of clinical microbiology
|April 4, 2024
概括
开发了一种用于在无菌板上检测细菌生长的算法. 它在24-48小时内达到高精度,使得早期监测和改进的实验室工作流程.
科学领域:
- 临床微生物学 临床微生物学
- 医学诊断 医学诊断 医学诊断
- 生物信息学是一种生物信息学.
背景情况:
- 阅读无菌板是临床细菌学实验室中耗时的工作,占工作负载的30-40%.
- 目前用于检测细菌生长的方法可能很慢,延迟了关键的临床决策.
研究的目的:
- 开发和评估一种算法,用于在无菌板上自动检测细菌生长.
- 评估算法的性能在各种时间点,包括早期检测.
- 探索临床细菌学中早期生长监测系统的潜力.
主要方法:
- 使用常见的细菌学介质和各种样本类型开发了一种用于检测细菌生长的算法.
- 总共有3844个带有临床样本的板块被成像了15次.
- 两个独立的微生物学家阅读这些图像,生成了99944个人类基本真理.
主要成果:
- 在48小时后,该算法实现了99.80%的灵敏度和91.97%的特异性.
- 在24小时内,灵敏度为99.08%,特异性为93.37%.
- 人类读者早在4小时就检测到增长,一半的阳性板块在6小时后显示增长.
结论:
- 开发的算法证明了在无菌板上自动检测细菌生长的高准确性.
- 早期生长监测是可行的,可能减少周转时间并帮助临床决策.
- 自动化系统可以通过对工作负载进行优先排序,并能够更早地将结果传达给临床医生,从而提供附加值.
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