Automated Scoring of Chromogenic Media for Detection of Methicillin-Resistant Staphylococcus aureus by Use of WASPLab Image Analysis Software |
| |
| Authors: | Matthew L. Faron Blake W. Buchan Chiara Vismara Carla Lacchini Alessandra Bielli Giovanni Gesu Theo Liebregts Anita van Bree Arjan Jansz Genevieve Soucy John Korver Nathan A. Ledeboer |
| |
| Affiliation: | aMedical College of Wisconsin, Milwaukee, Wisconsin, USA;bWisconsin Diagnostic Laboratories, Milwaukee, Wisconsin, USA;cA. O. Ospedale Niguarda Cà Granda, Milan, Italy;dPAMM Laboratory of Medical Microbiology, Veldhoven, Netherlands;eCHU de Quebec-Universite Laval, Quebec City, Quebec, Canada;fHamilton General Hospital, Hamilton, Ontario, Canada |
| |
| Abstract: | 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 (bioMérieux, Marcy l''Etoile, France), and CHROMagar 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. |
| |
| Keywords: | |
|
|
