EDUCATION::AI/ML IN THE LAB


workflow and reduce time to reporting, have also recently been widely accepted.”


“Labs benefit from providing better ‘tools’ to the workforce and software and machine learning opportunities represent a chance to focus the work we do on cases that are of criti- cal importance while targeting the work that we do in the lab to those aspects of reporting that require our attention,” Hansen added. Alanna Woodward, MLS(ASCP)CMSMCM, is Clinical Laboratory Microbiology Manager for UMC Health System, Lubbock, Texas, which has served as a primary testing site for an AI-enabled laboratory automation solu- tion. She speaks to her experience with the technology in triaging plates with growth and without growth, saying, “AI software is much more sensitive than the human eye and can detect colonies on a plate that may be missed by some medical laboratory scientists (MLS), especially when the MLS is tired or distracted. AI isn’t subject to the same constraints as humans, such as tired, distracted, aging eyesight, etc. AI has


Alanna Woodward, MLS(ASCP)CMSMCM


potential to be a great tool for the MLS if/when implemented well into the microbiology workflow.” “We are using it (the solution) to triage plates with growth


and without growth, so that the MLS can spend more time focusing on the cultures that need their expertise to interpret,” Woodward added. “It doesn’t make sense, especially with this technology, to utilize our highly trained MLS personnel to read/ interpret no growth plates.” Urine cultures, which make up a significant part of many


labs’ daily workload, is one prime area for automated sample triage because the majority of samples either have no growth or non-significant growth and, therefore, don’t require additional work-up, Story explains: “Batch review and release of large volumes of plates with non-significant growth can help medical laboratorians focus their time and expertise on more clinically relevant tasks and complex specimens,” Story said.


Achieve fast and accurate diagnosis Hansen points to the “speed and accuracy with which molecular testing for COVID-19 entered the clinical space” as an example of how AI/ML can be leveraged to bring needed diagnostics to the lab in a faster, more accurate and less expensive manner, compared to previous development routes. “Within the molecular space, AI was used to screen genetic material of COVID-19, providing a blueprint of the virus without ever having access to live virus. The application of AI for mo- lecular test development allowed COVID-19 PCR test kits to be developed in weeks versus months,” he states.


Differentiate between conditions Experts say advanced automation of laboratory technology enabled by AI and ML can help labs differentiate between conditions, providing patients with more accurate results in a shorter time period. While AI/ML is fairly new for analyzers used in routine di-


agnostics, Dominic Andrada, Sr., MS, Manager, Global Market Development, qPCR Molecular Diagnostics, Genetic Science Division, Thermo Fisher Scientific, says he has seen increased interest in the use of these technologies in future software ver- sions to analyze data from multiple markers in a test panel or screening test.


“I have only seen a few conference posters or examples where AI/ML could help,” Andrada explained. “It could potentially be used to help with pattern recognition of immunofluorescence assay (IFA) results for Antinuclear Antibodies (ANA) testing. This is a common test ordered to help with auto- immune diagnosis. This sounds intriguing to me, especially since IFA is a bit subjective and requires significant training to interpret the many different IFA patterns a sample could have.” Bruno Larida, MS, MBA, Vice President, Marketing, Seegene Technologies, notes


Dominic Andrada, Sr., MS,


how AI/ML can be used in the design of analyzers that can handle multiplex tests that interrogate multiple disease bio- markers in a single sample, stating:


“Recent reports show that the flu and


Bruno Larida, MS, MBA


COVID-19, for instance, can occur simulta- neously in a patient. Flu or other respiratory viruses, as well as COVID-19 symptoms, overlap, making a non-tested diagnosis, challenging. By using a multiplex test and analyzer, designed using AI, practitioners would be able to detect and differentiate between multiple targets, eliminating the need for multiple tests. Additionally, these diseases can be detected early and, therefore,


treated quickly. Using AI/ ML would also reduce the time taken to design and manufacture assays, from months to a few weeks.”


Verify results According to Eric Carlsgaard, MS, Senior Product Manager Informatics/Cloud at Beckman Coulter Diagnostics, in a world where there is a shortage of labor and an increased demand for testing, AI is poised to help by reducing manual intervention. He points specifically to AI automation in test result verification. “A routine urinalysis increases work- load when technicians must confirm the analyzer’s findings or identify unique par- ticle types (manual microscopic reviews are time-consuming and can take up to six times longer per sample than with an automated system1


),” Carlsgaard explains. Eric Carlsgaard, MS


“Digital flow morphology technology with Auto-Particle Recognition Software can enable laboratories to deliver standardized


results. This technology isolates, identifies, and characterizes urine particles to provide immediate, accurate, and reproducible results verified directly on the screen.”


Automate quality assurance checks Noting how clinical laboratories have taken significant steps in the use of rule-based functions to improve efficiencies, Carls- gaard describes how AI/ML can be used to streamline quality control (QC) checks.“For example, if QC is being run on an instrument every eight hours, it’s a standardized check. With auto QC, patient results are reviewed continually to determine if they are trending toward normal or extended ranges — so that the laboratorian can be alerted if some thresholds are crossed, which we know, based on rules and past data, may lead to problems with QC. Because if we wait for the regular QC, which is conducted every eight hours, we may find out what’s wrong after it’s too late and must invalidate and re-run a significant number of tests,” he said.


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