BEST PRACTICES :: ARTIFICIAL INTELLIGENCE WASPLab Studio; image courtesy of COPAN Diagnostics.


results with no technologist intervention. Laboratories can also use the software to gather and analyze speci- men results together with pertinent clinical patient information to optimize their algorithms. For exam- ple, algorithms can combine culture results with the sex and age of the patient to determine if small numbers of organisms detected in urine cultures need to be screened for group B streptococci. In general terms, here’s how the software works: First a ‘time zero’ image is taken of the culture plate, then at user-defined incu- bation times additional images are taken and compared to the time zero image. Specifically, for chromogenic agars, the first question that is then asked is, “Is there something present now that was not present at time zero?” If the answer is yes, then the next question is, “What is the color of what is now present?” The final question that is asked is, “What hue is the color that is now present?” If the chromogenic media has growth, that growth is green, and the green colonies are the ‘right’ color (lime green for this particular media), then the AI tells you that the culture is presumptively positive. Conversely, if there is no growth or if the growth present is not the color and hue needed to be considered positive, then the AI will pre-sort that the cul- ture as most likely negative. In addition, the software can determine colony counts of urine cultures and pre-sort these cultures into categories of: 1. No growth; 2. 100-1000 CFU/mL; 3. 1000-10,000 CFU/mL; 4. 10,000-100,000 CFU/mL; and 5. > 100,000 CFU/mL with great accuracy. Laboratory staff will also gain efficiencies as they will be able to focus on plates that AI/IA cannot yet deal with. In addition, using these algorithms there is not the vari- ability that we see with human interpretation, i.e., the algorithms always make the same decision each time and never deviate from standard operating procedure.


Training and quality assurance An extension of the above discussed advances of FLA, include an added advantage of utilizing the system for efficient training and quality assurance (QA) activities. Such activities would include utiliz- ing images of known organisms and cultures for more efficient training of new staff members. Train- ing technologists to recognize normal organism morphology could be expedited using the plethora of stored images of previous cultures. Technologists would be able to visualize multiple images taken over time on previous cultures along with the progression of culture results to learn how clinical microbiology decisions are made in the work up of various culture types. It could take months or longer for an isolate of Listeria monocytogenes, for example, to come into the laboratory, but with stored images along with Gram stain images, new staff can be trained to recognize these and other less commonly isolated organisms the first time they see it in a real time culture. Laboratory QA is designed to detect, reduce and correct deficiencies and errors in laboratory practices to release quality patient results and involves all parts of testing: pre-analytical, analytical, and post-analytical processes. CLIA regulations (Subpart P) address specific quality assurance requirements and The Code of Federal


WASPLab Incubator; image courtesy of COPAN Diagnostics.


Regulations (42 CFR 493) states laboratories “must estab- lish and follow written policies and procedures for a com- prehensive quality assurance program that is designed to monitor and evaluate the ongoing and overall quality of the total testing process.” Further it states that the QA program must: 1. Assess the effectiveness of the laboratory’s policies and procedures; 2. Identify and correct problems; 3. Assure the accurate, reliable, and prompt reporting of test results; and 4. Assure the adequacy and competency of the staff.4 Liquid based-microbiology (LBM) collection and transportation devices together with FLA can assist in


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