Med Tech

Automation in Clinical Microbiology

Traditional Microbiology techniques such as microscopy, culture studies, staining and serology studies are used to test and detect the presence of the pathogen in the sample. Other newer automated rapid identification systems using biosensors and radioactivity also help in determining the causative organism in a short span of time. 

Technologies like Cartridge Based- and True- Nucleic Acid Amplification Technique and Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) help in the rapid molecular diagnosis of tuberculosis and COVID-19 infections. Omics based analysis using MALDI-TOF (Matrix Assisted Laser Desorption/Ionisation Time of Flight) Spectroscopy, Next Generation Sequencing (NGS) and Whole Genome Sequencing (WGS) can identify which strain of the microorganism is causing the infection.

While these techniques are extremely essential in determining the presence and identification of the disease causing organism, they take a lot of time to process and generate a large amount of data, especially with the advent of bioinformatics. Microscope and culture studies are also prone to error and can put the safety of the technical staff at risk while handling deadly airborne pathogens such as the SARS-CoV-2 virus. 

Automation is the technology by which a process or procedure is performed without human assistance. In the laboratory context this means that every switch from manual work to machines can be called automation. While automation has been a part of the laboratories in the form of the centrifuge, continuous growth monitoring and RT-PCR, total lab automation is still in its nascent phase. 

In a webinar conducted by MedPiper Technologies and JournoMed, expert speaker, Dr. Suranjan Pal explained how total lab automation is the future of clinical microbiology. One of the ways automation can improve a clinical microbiology lab is by reducing the amount of time needed to generate results. Total lab automation helps in the faster processing of the samples by eliminating repetitive practices (laboratory noise). 

Specimens are processed without delay upon receipt in the laboratory, with the automated selection of appropriate media for individual specimens. This also includes labeling the plates with barcodes, and inoculation of the media before they are transported to incubators on track systems. The faster the results, the faster the diagnosis, the faster the treatment and recovery. Thus automation can improve the efficiency of a laboratory due to less chances of human error. 

Full laboratory automation moves plates from the processing area to incubators, eliminating delays in incubation. The plates are incubated under ideal growth conditions of stable temperature and atmosphere because the doors are not opened, and growth is monitored through images taken by sophisticated camera systems at predetermined time periods. Thus, significant growth or change in growth patterns can be detected earlier, with improved recovery of both common and slow-growing pathogens

Via standardised algorithms, automation can capture fastidious (organisms that have particular types of nutritional requirements) disease causing organisms and can also help in enhanced recovery. Using Artificial Intelligence and Machine Learning, automation can closely monitor and interpret bacterial culture growth. Imaging algorithms and software allows to capture subtle differences in morphology of the colonies and differentiate between mixed cultures. These algorithms can also help quantify bacterial growth. 

In general, microbiology laboratories already work with a computerized laboratory information system. Its function is to enable administration of patient samples, documentation of arrival and processing of samples and follow-up work. It is used to create lab reports. By interfacing total lab automation with the laboratory information system, results can be generated swiftly. Automation can also process a large number of samples at a go. Automated processing of the samples will also reduce the interaction between the technical staff and the probable highly communicable microorganisms in the plate, thus keeping the safety risk of the handlers in mind. 

As more and more biorepositories and databases are expanding there is an increased need for automation, and machine learning. However, some of the main concerns include the lack of standardization of these technologies, sustainability and data privacy. Many scientists have also expressed concern over the machines replacing them if automated interpretation is set in place. 

Copan Diagnostics’ WASPLab and Becton Dickinson’s Kiestra TLA are some of the examples of total lab automation in clinical microbiology practice which reduce the report output by a day. These labs are not yet available in India. Despite the fact that the incubation and plate sorting are done by the machines, the clinical microbiologists are still needed for the interpretation of the reports produced and to relay the information to the clinicians. Hence the role of clinical microbiologists remains just as essential even with the advent of total lab automation

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