BEST PRACTICES :: ARTIFICIAL INTELLIGENCE
AI and interpretive algorithms Obtaining accurate and efficient results in clinical microbiology
By Susan Sharp, PhD A
n innovative and collaborative approach to pre-analytics has resulted in original devices that have made microbiology collection pro- cesses simple and easy. Many of these collection and transport systems have been proven to advance the quality of traditional and contemporary microbiol- ogy assays. Flocked swabs and other liquid-based microbiology collection devices together with full laboratory automation (FLA) for microbiology, allow clinical laboratories to fully automate their culture testing. FLA includes specimen processing, smart incu- bation, digital imaging, and effective algorithms for automatic segregation of bacterial cultures, followed by automated colony selection and setting up of identification and susceptibility testing.
Collections devices and full laboratory automation Automatic specimen processor units are a solution for preanalytical microbiology. These are open platforms, modular instruments, and can address all aspects of automated microbiology specimen processing: planting and streaking, Gram slide preparation and enrichment broth inoculation, and more. These systems can automate the workflow of the laboratory and allow the freedom to walk away from specimen set up and focus on higher level tasks. Most importantly, automated specimen pro- cessing is what microbiologists have asked for in their ideal laboratory.
Testing efficiencies, quality, and safety FLA allows samples to be evaluated faster and with- out the need for additional staffing. The system allows for earlier growth detection as cul- ture plates are in a continuous incubation situation at the correct temperature and atmosphere for optimal growth. There is no constant opening of the incubator doors to retrieve plates subjecting the cul- tures to suboptimal conditions, and plates are not left on the bench top for hours without proper incubation and atmo- spheric requirements. These efficiencies will allow for a new paradigm for what we think we know about incubation times. For example, traditionally urine cultures need to be incubated for 16-18 hours prior to the selection of colonies for further identification and susceptibility testing. With FLA and continuous incubation these cultures can be read as early as 10-12 hours. This new shift will occur with other types of specimens as we see improved bacterial growth of all cultures through defined, uninterrupted incubations. FLA allows for
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safe work up by laboratory staff without the need to be exposed to plates possibly growing highly infectious agents, and efficiencies will be gained by never needing to touch a negative plate again. The system also offers increased traceability using a bi-directional connection with the laboratory information system (LIS).
Artificial intelligence
Automated specimen processing and reading sys- tems can be used to optimize laboratory resources for increased productivity. New AI/IA software uses artificial intelligence (AI) and interpretive algorithms (IA) to aid laboratory personnel with culture reading to allow for further optimization in the laboratory freeing up staff to concentrate on more difficult tasks.
AI/IA systems use a selection of highly sophisticated algorithms that will pre-assess, and pre-sort culture plates allowing microbiology laboratories to then read, interpret, and segregate bacterial cultures with the click of a button. As examples, algorithms that are currently on the market include: 1. Segregation of chromogenic media for the detection of
methicillin-resistant Staphylococcus
aureus (MRSA); 2. Digital analysis of chromogenic media for vancomycin-resistant Enterococcus (VRE) screens; and 3. Pre-sorted digital segregation of urine culture quantitation.
Several currently available algorithms have been submitted to the Food and Drug Administration to allow for auto-verification and automatic release of
WASP loop; image courtesy of COPAN Diagnostics.
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