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从阳性血液培养物中对格拉姆染色体的机器辅助解释的性能评估

Christian Walter^1,2, Christoph Weissert³, Eve Gizewski⁴

¹Department of Infectious Diseases, Medical Microbiology and Hygiene, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.

Journal of clinical microbiology

|March 20, 2024

概括

使用深层卷积神经网络 (CNN) 的新人工智能系统显示,它有望在阳性血液培养 (PBC) 中自动化格拉姆染色分析,提高血液感染的诊断速度和准确性.

科学领域:

医学诊断医学诊断医学诊断
计算病理学计算病理学
微生物学微生物学

背景情况:

从阳性血液培养 (PBCs) 中手动显微镜检查格拉姆染色体对于诊断血液感染至关重要,但是劳动密集型,耗时且主观的.
需要自动化系统来提高临床实验室的效率和客观性.

研究的目的:

评估数字显微镜系统与深层卷积神经网络 (CNN) 结合,以协助从PBC中解释格拉姆染色.
评估系统的准确性,可重复性和日常实验室使用的潜力.

主要方法:

美国有线电视新闻网 (CNN) 接受了训练,将格拉姆染色图像分为七个类别.
该系统扫描和分析了1,555个染PBC幻灯片.
结果与手动显微镜和矩阵辅助激光脱/离子化飞行时间质谱法 (MALDI-TOF MS) 识别进行了比较.

主要成果:

人工智能辅助的解释表明,对各种微生物的手动显微镜具有很高的一致性 (例如,在群集中的格拉姆阳性菌中有95.8%).
与MALDI-TOF MS.相比,观察到类似的协议.
该系统表现出极好的可重复性和可重复性,检测极限为10^5 CFU/mL.

结论:

基于CNN的格拉姆染色解释系统显示了在诊断血液感染方面有很大的潜力.
虽然该技术尚未准备好全面临床实施,但它为实验室诊断的未来进步提供了有希望的途径.

关键词:

格拉姆斑点的染色体是什么人工智能的人工智能是人工智能.自动化显微镜自动化显微镜自动化自动化自动化自动化血液文化血液文化血液感染血液感染深度学习是一种深度学习.数字化数字化机器学习是机器学习.神经网络的神经网络的神经网络

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