EDUCATION::AI/ML IN THE LAB


Enhance staff training Another area where Woodward and her team at UMC Health System are leveraging AI and ML is in the training of new lab employees. Because the solution they are using captures images of culture growth, they can save and store rare organisms for training purposes. “There are situations that only arise every 5+ years in the microbiology laboratory, and now we can use the software to give our new people a leg-up in recognizing these situa- tions,” Woodward explained. “Prior to the AI/ML, we depended heavily on having a long-term experienced MLS available to recognize and help the newest MLS. This meant reading cultures for multiple days and scheduling multiple people to read behind each other to not miss these rare situations. Now, with the AI/ML, we can give the newest MLS employees that knowledge earlier in their career before they’ve potentially missed a critical result for a patient.” When asked where they see AI/ML being used in the future when it comes to lab analyzers, our experts pointed to everything from research and development (R&D) efforts to identifying organisms in patient samples.


Rapid R&D “Machine learning-based diagnostic analyzers will be the key to simplifying and automating the R&D process, allowing sci- entists to respond rapidly to widespread testing needs,” said Larida, pointing to the use of ML and AI in the battle against SARS-CoV-2. He notes how scientists have already leveraged these advanced technologies in diagnosis, treatment and vaccine development efforts throughout the COVID-19 pandemic.


Colony identification Woodward believes the “sky’s the limit” when it comes to the application of AI/ML in the lab environment. She hopes to see AI/ML becoming more robust in recognizing colors and growth patterns to determine how many colony types are growing in any given culture. “AI/ML is able to offer identification suggestions, based on the color that grows on that media type. However, I’d like to see this expanded out to other routine media types so that we can utilize the AI/ML in more culture sources such as tissue, wound, respiratory, etc.,” Woodward explains. “I’m not sure I’m totally comfortable with AI/ML being able to identify organ- isms from the routine agar yet, but being able to differentiate multiple colony types and give the MLS an idea of how many organisms they may be looking for would be very helpful in interpreting cultures on the first read.”


Meaningful worklists Story sees AI and ML being leveraged to “automatically organize specimens into meaningful worklists driven by user-defined expert rules for critical criteria such as ‘high risk patients’ or ‘complex specimens.’” She explains how this would help labs professionals work more efficiently by prioritizing their efforts on the most critical and complex specimens, stating, “Reading plates is one of the laboratory tasks that requires the most skill and, given the decrease of skilled technicians entering the workforce and the strain on resources created by the COVID-19 pandemic, it’s that much more critical for labs to be able to be more efficient by taking advantage of automation.”


Disease differentiation As Andrada noted, AI/ML holds tremendous potential for running tests for autoimmune diagnosis. He said, “I expect to see AI/ML used in future algorithmic analysis of data to help or increase the confidence of diagnosing a specific autoimmune disease from several potential disease states.”


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Patient feedback loops Carlsgaard believes the benefits of AI-enabled technologies will go beyond operational efficiency to transform patient care de- livery. “With AI, we can detect patterns hidden in large amounts of data and predict the future, which is the backbone of both automation and decision support. AI-enabled technologies, in the future, will come together to create data feedback loops to help improve not only efficiencies but transform patient care from ‘sick care’ to ‘well care.’ The journey has already begun,” he said.


Considerable change Hansen comments on how the application of AL/ML in clinical microbiology could contribute to the considerable change we are already seeing in the field, stating, “The reality of the global Sars2-covid-19 pandemic has forever shaped the field of clinical microbiology and placed emphasis on accuracy, efficiency, and support of a critical underappreciated workforce in need of new diagnostics support tools. However, it’s also the craziest of times when one realizes the greatest opportunities for change and adaptation.”


REFERENCES


1. Block D. Automated urinalysis in the clinical lab. Medical Laboratory Observer. https://www.mlo-online.com/home/article/13004799/automated- urinalysis-in-the-clinical-lab.Published October 19, 2012. Accessed August 2, 2021.


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