Department of Pathology, Case Western Reserve University, Cleveland, Ohio, USA
J Clin Microbiol. 2020 May 26;58(6). doi: 10.1128/JCM.00511-20.
Artificial intelligence (AI) is increasingly becoming an important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these solutions have the potential to improve a test's turnaround time, quality, and cost. A study by Mathison et al. used computer vision AI (B. A. Mathison, J. L. Kohan, J. F. Walker, R. B. Smith, et al., J Clin Microbiol 58:e02053-19, 2020, https://doi.org/10.1128/JCM.02053-19), but additional opportunities for AI applications exist within the clinical microbiology laboratory. Large data sets within clinical microbiology that are amenable to the development of AI diagnostics include genomic information from isolated bacteria, metagenomic microbial findings from primary specimens, mass spectra captured from cultured bacterial isolates, and large digital images, which is the medium that Mathison et al. chose to use. AI in general and computer vision in specific are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology.
人工智能(AI)正日益成为临床微生物学信息学的一个重要组成部分。研究人员、微生物学家、实验室技术人员和诊断医生对基于人工智能的检测感兴趣,因为这些解决方案有可能提高检测的周转时间、质量和成本。Mathison 等人的一项研究使用了计算机视觉人工智能(B. A. Mathison、J. L. Kohan、J. F. Walker、R. B. Smith 等人,J Clin Microbiol 58:e02053-19, 2020, https://doi.org/10.1128/JCM.02053-19),但临床微生物学实验室中还存在其他人工智能应用的机会。临床微生物学中适合开发人工智能诊断的大型数据集包括从分离细菌获得的基因组信息、从原始标本获得的宏基因组微生物发现、从培养的细菌分离物捕获的质谱以及 Mathison 等人选择使用的大型数字图像。人工智能通常是临床微生物学家需要研究、开发和实施的新兴工具,以便改进临床微生物学。