通过深度学习估计细菌殖民地规模的增长
Sára Ágnes Nagy1, László Makrai2, István Csabai3
1Centre for Bioinformatics, University of Veterinary Medicine, 1078, Budapest, Hungary.
BMC microbiology
|October 25, 2023
概括
卷积神经网络 (CNN) 准确地检测细菌殖民地,并从图像中预测生长率. 这种人工智能方法为细菌学提供了有效的工具,有助于致病性和食品安全研究.
科学领域:
- 微生物学 微生物学
- 计算机科学 计算机科学
- 生物信息学是一种生物信息学.
背景情况:
- 细菌生长速度对于病原性和食品安全至关重要.
- 监测细菌生长提供了重要的医疗和兽医数据.
- 准确量化细菌生长动态对于研究至关重要.
研究的目的:
- 开发和验证卷积神经网络 (CNN) 用于细菌殖民地检测和增长率估计.
- 分析殖民地密度和利芬素预处理对细菌生长动态的影响.
- 评估AI驱动图像分析在细菌学研究中的有效性.
主要方法:
- 在固体介质上手动注释细菌培养的图像上培训CNN.
- 估计细菌殖民地大小和生长速度,使用金黄色葡萄球菌 (Staphylococcus aureus) 的图像序列.
- 使用线性和混合效应模型来分析增长数据和影响因素.
主要成果:
- CNNs准确地检测到了细菌殖民地,并预测了生长率.
- 对照种植物的平均增长率估计在前24小时内为60.3单位/小时.
- 殖民地生长率因邻近殖民地增加而降低,特别是在对照组,以及因里芬素预处理 (36.5单位/小时).
结论:
- 基于CNN的细菌殖民地检测是一种准确和有效的方法.
- 对细菌殖民地生长动态的AI分析为细菌学提供了有价值的工具.
- 这种方法可以显著推进致病性和食品安全研究.
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