Automated Scoring of Chromogenic Media for Detection of Methicillin-Resistant Staphylococcus aureus by Use of WASPLab Image Analysis Software
Automated Scoring of Chromogenic Media for Detection of Methicillin-Resistant Staphylococcus aureus by Use of WASPLab Image Analysis Software
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DOI：
10.1128/jcm.02778-15
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发表时间：
2016-03-01
影响因子：
9.4
通讯作者：
Ledeboer, Nathan A.
中科院分区：
文献类型：
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作者：
Faron, Matthew L.;Buchan, Blake W.;Ledeboer, Nathan A.
最近，已经开发了用于为临床微生物学创建完全实验室自动化的系统。这些系统允许标本处理、标本孵育和细菌生长成像的自动化。在这项研究中，我们使用WASPLab验证软件，通过识别着色菌落来区分和分离阳性和阴性显色耐甲氧西林金黄色葡萄球菌（MRSA）平板。本研究共入组了57，690份提交用于MRSA筛查的拭子。4个研究中心按照其标准治疗入组标本。这些研究中心使用的显色琼脂包括MRSA Select（Bio-Rad Laboratories，雷德蒙，WA）、chromID MRSA（bioMerieux，Marcy l 'Etoile，France）和CHRO-Magar MRSA（BD Diagnostics，Sparks，MD）。使用WASPLab对样本进行平板培养和孵育。数码相机在0和16至24小时拍摄图像，WASPLab软件基于色调、饱和度和值（HSV）评分确定阳性菌落的存在。如果HSV评分在规定的阈值内，则将平板判定为阳性。将数字分析的性能与手动阅读进行比较。总体而言，数字软件的灵敏度为100%，特异性为90.7%，所有研究中心的特异性范围为90.0 - 96.0。使用三种不同琼脂的结果相似，灵敏度为100%，特异性范围为90.7%至92.4%。这些数据表明，自动化数字分析可用于准确地从阴性显色琼脂培养物中分选阳性，而不管产生的色素沉着。
Recently, systems have been developed to create total laboratory automation for clinical microbiology. These systems allow for the automation of specimen processing, specimen incubation, and imaging of bacterial growth. In this study, we used the WASPLab to validate software that discriminates and segregates positive and negative chromogenic methicillin-resistant Staphylococcus aureus (MRSA) plates by recognition of pigmented colonies. A total of 57,690 swabs submitted for MRSA screening were enrolled in the study. Four sites enrolled specimens following their standard of care. Chromogenic agar used at these sites included MRSASelect (Bio-Rad Laboratories, Redmond, WA), chromID MRSA (bioMerieux, Marcy l'Etoile, France), and CHRO-Magar MRSA (BD Diagnostics, Sparks, MD). Specimens were plated and incubated using the WASPLab. The digital camera took images at 0 and 16 to 24 h and the WASPLab software determined the presence of positive colonies based on a hue, saturation, and value (HSV) score. If the HSV score fell within a defined threshold, the plate was called positive. The performance of the digital analysis was compared to manual reading. Overall, the digital software had a sensitivity of 100% and a specificity of 90.7% with the specificity ranging between 90.0 and 96.0 across all sites. The results were similar using the three different agars with a sensitivity of 100% and specificity ranging between 90.7 and 92.4%. These data demonstrate that automated digital analysis can be used to accurately sort positive from negative chromogenic agar cultures regardless of the pigmentation produced.