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Integrating automation into clinical practice

The microbiology laboratory now has access to a level of automation thus far only seen in the chemistry and hematology sections of the clinical laboratory. These transitions have repeatedly shown to enhance the level of patient care when properly implemented into the laboratory workflow.

The field of clinical microbiology is rapidly evolving with several recent technological advances. Automation has transformed clinical laboratory disciplines like chemistry and hematology, but has until recently left the microbiology space relatively untouched.

Automation in microbiology is not simply automating the traditional practices of processing specimens and incubating cultures – that would not be transformative. Automation should be viewed as an all-encompassing approach to processing samples and communicating information that enable laboratory technologists to perform skilled tasks in ways not historically possible. This is why automation was recently described as a paradigm shift in clinical microbiology, representing the beginning of the future.

A new era of technology
Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. Against this background of painfully short staffing, concern about the impact of machine learning and Artificial Intelligence (AI) on technologists’ continued employment and job satisfaction has crept into watercooler conversation. The field is only slowly approaching total laboratory automation as AI stands to screen out some of the least interesting (yet highest volume) work of the laboratory.

Currently, there are two commercially available Food and Drug Administration (FDA)-approved microbiology laboratory automation platforms. These systems are only for the processor, incubator, and imaging software. Procedures guiding culture interpretation and reporting vary widely among microbiology laboratories and require in-house validation when implementing laboratory automation.

Digital imaging and machine learning provide an opportunity to improve upon the efficiency and quality of culture interpretation and reporting. Machine learning has already made headway in other areas of medicine, where large amounts of complex data, especially images, must similarly be distilled into a clinical-grade judgment. For urine cultures with no growth, which may constitute the majority of a laboratory’s workload, there is an opportunity to auto-verify these results without human intervention using appropriate image analysis software. For positive growth plates, an AI screen for significant growth versus insignificant growth would allow certain classes of positive plates to be auto-resulted as well.

Dr Rahul Kamble
Consultant Microbiologist and Infection Control,
Lilavati Hospital and Research Centre, Mumbai

“The recent advances in microbiology laboratory have revolutionized the identification and surveillance of infectious diseases. As laboratory automation, rapid diagnostic platforms especially point-of-care tests and molecular microbiology techniques are deployed globally, it has enabled physicians to make effective clinical decisions and contain infections, saving lives and lowering costs.”

Recently, researchers evaluate WASPLab automated digital image analysis software for urine cultures at three microbiology laboratories. Results were compared to institution-specific laboratory protocols. On top of the identification of culture plates with growth or no growth of microorganisms, they added a second layer of analysis via machine learning to quantify microorganisms and categorize cultures as significant growth or insignificant growth based on laboratory-specific thresholds. Put into practice, this additional analysis furthers efficiency by focusing technologist time on working up positive urine cultures and eliminating time spent reading urine cultures with no growth or insignificant growth.

Digital images and machine learning of urine cultures was compared with plate reading by laboratory technologists using the same digital images. Discrepant analysis showed that for cultures that were negative by manual reading and positive by automation, over 90 percent of the discrepancies were caused by mixed cultures where the total colony count pushed them over the threshold into the significance category, but the laboratory interpretative reporting protocol deemed them insignificant. Although intra-observer variability was not tested, less than 10 percent of discrepant results were due to differences in colony counts between technologists and the machine learning software.

This introduces the question of how laboratories can implement this type of technology in practice. Image analysis software is not currently FDA approved, so the algorithm it deploys qualifies as a high-complexity laboratory-developed test when used to make definitive calls about microorganism presence/absence or culture significance. In this context, the end user need not understand the internal workings any more than they understand the inner workings of most computers. Additionally, as with most laboratory software, manufacturer assistance is provided in training the algorithm.

The automation monolith is evolving laboratory practice to where one can imagine the microbiology lab of the future. Urine specimens arrive in the lab and are immediately placed into the laboratory automation system, which understands how to set up a culture according to the sample’s barcode. Cultures are read around the clock using machine learning image analysis that is trained according to local laboratory practice. Negative cultures are auto-verified in the laboratory information system, and agar plates are discarded from the laboratory automation system without technologist intervention.

Now imagine this process expanded to other specimen types, such as respiratory specimens and sterile-source cultures. Image analysis and machine learning could reduce repetitive tasks, use technologist skills to their fullest, and provide clinicians more timely results to improve patient care for a majority of a typical laboratory’s culture volume. This will happen while maintaining a need for the skilled, hands-on, satisfying elements that attract newcomers to clinical microbiology and laboratory medicine.

Automation is not a choice any more
The clinical microbiology laboratory now has access to a level of automation thus far only seen in the chemistry and hematology sections of the clinical laboratory. These transitions have been repeatedly shown to enhance the level of patient care when properly implemented into the laboratory workflow. Conversely, with the rapid encroachment of these new technologies comes potential downfalls, including cost and challenges with training laboratory staff. Collectively, the clinical microbiology laboratory is coming into a new era of technology and patient care that will bring about dramatic changes to conventional testing and organism identification.

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