Automated Incubation and Digital Image Analysis of Chromogenic Media Using Copan WASPLab Enables Rapid Detection of Vancomycin-Resistant Enterococcus

Development and evaluation of an artificial intelligence for bacterial growth monitoring in clinical bacteriology

In clinical bacteriology laboratories, reading and processing of sterile plates remain a significant part of the routine workload (30%-40% of the plates). Here, an algorithm was developed for bacterial growth detection starting with any type of specimens and using the most common media in bacteriology. The growth prediction performance of the algorithm for automatic processing of sterile plates was evaluated not only at 18-24 h and 48 h but also at earlier timepoints toward the development of an early growth monitoring system. A total of 3,844 plates inoculated with representative clinical specimens were used. The plates were imaged 15 times, and two different microbiologists read the images randomly and independently, creating 99,944 human ground truths. The algorithm was able, at 48 h, to discriminate growth from no growth with a sensitivity of 99.80% (five false-negative [FN] plates out of 3,844) and a specificity of 91.97%. At 24 h, sensitivity and specificity reached 99.08% and 93.37%, respectively. Interestingly, during human truth reading, growth was reported as early as 4 h, while at 6 h, half of the positive plates were already showing some growth. In this context, automated early growth monitoring in case of normally sterile samples is envisioned to provide added value to the microbiologists, enabling them to prioritize reading and to communicate early detection of bacterial growth to the clinicians.

The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology

The growing transition to digital microbiology in clinical laboratories creates the opportunity to interpret images using software. Software analysis tools can be designed to use human-curated knowledge and expert rules, but more novel artificial intelligence (AI) approaches such as machine learning (ML) are being integrated into clinical microbiology practice. These image analysis AI (IAAI) tools are beginning to penetrate routine clinical microbiology practice, and their scope and impact on routine clinical microbiology practice will continue to grow. This review separates the IAAI applications into 2 broad classification categories: (i) rare event detection/classification or (ii) score-based/categorical classification. Rare event detection can be used for screening purposes or for final identification of a microbe including microscopic detection of mycobacteria in a primary specimen, detection of bacterial colonies growing on nutrient agar, or detection of parasites in a stool preparation or blood smear. Score-based image analysis can be applied to a scoring system that classifies images in toto as its output interpretation and examples include application of the Nugent score for diagnosing bacterial vaginosis and interpretation of urine cultures. The benefits, challenges, development, and implementation strategies of IAAI tools are explored. In conclusion, IAAI is beginning to impact the routine practice of clinical microbiology, and its use can enhance the efficiency and quality of clinical microbiology practice. Although the future of IAAI is promising, currently IAAI only augments human effort and is not a replacement for human expertise.

Copan Walk Away Specimen Processor (WASP) Automated System for Pathogen Detection in Female Reproductive Tract Specimens

Objective

Automation is increasingly being applied in clinical laboratories; however, preanalytical processing for microbiology tests and screening is still largely performed using manual methods owing to the complex procedures involved. To promote automation of clinical microbiology laboratories, it is important to assess the performance of automated systems for different specimen types separately. Therefore, the aim of this study was to explore the potential clinical application of the Copan Walk Away Specimen Processor (WASP) automated preanalytical microbiology processing system in the detection of pathogens in female reproductive tract specimens and its feasibility in optimizing diagnostic procedures.

Methods

Female reproductive tract specimens collected from pregnant women at their first obstetric check-up were inoculated into culture media using the Copan WASP automated specimen processing system and were also cultured using a conventional manual inoculation method. After 48 h of culture, the growth of colonies was observed, and the types of bacteria, number of colonies, and efficiency in isolating single colonies were compared between the automated and manual groups. The specimens collected from the WASP system using the Copan-ESwab sample collection tubes were further analyzed for the presence of Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Ureaplasmaurealyticum (UU) via fluorescence quantitative polymerase chain reaction (qPCR) and an immunochromatographic assay to investigate the feasibility of this method in optimizing detection of these common pathogens of the female reproductive tract.

Results

Compared with the manual culture method, the Copan WASP microbiology automation system detected fewer bacterial types (P<0.001) and bacterial colonies (P<0.001) but had a higher detection rate of single colonies (P<0.001). There was no significant difference in the detection rates of common pathogens encountered in clinical obstetrics and gynecology, including group B Streptococcus (GBS) (P=0.575) and Candida (P=0.917), between the two methods. Specimens collected in the Copan-ESwab tubes could be used for screening of GBS and CT via fluorescence-based qPCR but not with immunochromatography. However, UU and NG were not detected in any sample with either method; thus, further validation is required to determine the feasibility of the Copan system for screening these pathogens.

Conclusion

The Copan WASP microbiology automation system could facilitate the optimization of diagnostic procedures for detecting common pathogens of the female reproductive system, thereby reducing associated costs.

Performance of Fully Automated Antimicrobial Disk Diffusion Susceptibility Testing Using Copan WASP Colibri Coupled to the Radian In-Line Carousel and Expert System

The purpose of the present study was to assess the agreement at the categorical level between the Vitek 2 system and the Colibri coupled to the Radian under real routine laboratory conditions. The 675 nonduplicate clinical strains included in this study (249 Enterobacterales isolates, 198 Pseudomonas aeruginosa, 107 Staphylococcus aureus, 78 coagulase-negative staphylococci, 38 Enterococcus faecalis, and 5 Enterococcus faecium) were isolated from nonconsecutive clinical samples referred to our laboratory between June and November 2020. In addition, 43 carbapenemase-producing Enterobacterales (CPE) formerly identified and stored in our laboratory were added to the panel, for a total of 718 strains. The overall categorical agreements between the two compared methods were 99.3% (4,350/4,380; 95% CI 99% to 99.5%); 98.6% (2,147/2,178; 95% CI 98.0% to 99.0%); 99.4% (1,839/1,850; 95% CI 98.9% to 99.7%); and 99.4% (342/344; 95% CI 97.9% to 99.8%) for Enterobacterales, P. aeruginosa, Staphylococcus spp., and Enterococcus spp., respectively. The most important cause of the very major errors encountered on the Vitek 2 for P. aeruginosa (62%, 13/21) was related to the presence of heteroresistant populations. Among the 43 CPE included in this study, one OXA-48-like, and one OXA-181-like were missed by the Vitek 2, even by rigorously applying the CPE screening cutoffs defined by EUCAST. The Colibri coupled to the Radian provide a fully automated solution for antimicrobial disk diffusion susceptibility testing with an accuracy that is equal to or better than that of the Vitek 2 system.

Performance of PhenoMatrix for the detection of Group B Streptococcus from recto-vaginal swabs

We assessed the performance of PhenoMatrix digital imaging software in detection of Group B Streptococcus from recto-vaginal swabs plated on a specific chromogenic medium, using the WASP automated processor. PhenoMatrix algorithm showed a sensitivity of 100% and a specificity of 64.5%. False-positive results were mainly due to commensal viridans streptococci.

Performances of automated digital imaging of Gram-stained slides with on-screen reading against manual microscopy

The objective of this study was to evaluate the performances of the automated digital imaging of Gram-stained slides against manual microscopy. Four hundred forty-three identified Gram-stained slides were included in this study. When both methods agreed, we considered the results as correct, and no further examination was carried out. Whenever the methods gave discrepant results, we reviewed the digital images and the glass slides by manual microscopy to avoid incorrectly read smears. The final result was a consensus of multiple independent reader interpretations. Among the 443 slides analyzed in this study, 101 (22.8%) showed discrepant results between the compared methods. The rates of discrepant results according to the specimen types were 5.7% (9/157) for positive blood cultures, 42% (60/142) for respiratory tract specimens, and 22% (32/144) for sterile site specimens. After a subsequent review of the discrepant slides, the final rate of discrepancies dropped to 7.0% (31/443). The overall agreement between the compared methods and the culture results reached 78% (345/443) and 79% (349/443) for manual microscopy and automated digital imaging, respectively. According to culture results, the specificity for automated digital imaging and manual microscopy were 90.8% and 87.7% respectively. In contrast, sensitivity was 84.1% for the two compared methods. The discrepant results were mostly encountered with microorganism morphologies of rare occurrence. The results reported in this study emphasize that on-screen reading is challenging, since the recognition of morphologies on-screen can appear different as compared to routine manual microscopy. Monitoring of Gram stain errors, which is facilitated by automated digital imaging, remains crucial for the quality control of reported Gram stain results.

Mini Review: Clinical Routine Microbiology in the Era of Automation and Digital Health

Clinical microbiology laboratories are the first line to combat and handle infectious diseases and antibiotic resistance, including newly emerging ones. Although most clinical laboratories still rely on conventional methods, a cascade of technological changes, driven by digital imaging and high-throughput sequencing, will revolutionize the management of clinical diagnostics for direct detection of bacteria and swift antimicrobial susceptibility testing. Importantly, such technological advancements occur in the golden age of machine learning where computers are no longer acting passively in data mining, but once trained, can also help physicians in making decisions for diagnostics and optimal treatment administration. The further potential of physically integrating new technologies in an automation chain, combined to machine-learning-based software for data analyses, is seducing and would indeed lead to a faster management in infectious diseases. However, if, from one side, technological advancement would achieve a better performance than conventional methods, on the other side, this evolution challenges clinicians in terms of data interpretation and impacts the entire hospital personnel organization and management. In this mini review, we discuss such technological achievements offering practical examples of their operability but also their limitations and potential issues that their implementation could rise in clinical microbiology laboratories.

Impact of Total Laboratory Automation on Turnaround Times for Urine Cultures and Screening Specimens for MRSA, ESBL, and VRE Carriage: Retrospective Comparison With Manual Workflow

Using computerized time-stamps, we compared the turnaround-times (TAT) for urine samples and screening ESwabs of MRSA, VRE, and ESBL carriage in the bacteriology laboratory of Geneva University Hospitals between January and December 2017 (period preceding the implementation of the WASPLab^TM) with the same specimen types analyzed between January and December 2019 (period after the implementation of the automation). During both 1-year periods, a total of 98'380 specimens were analyzed (48'158 in 2017 vs. 50'222 in 2019). On the WASPLab^TM, all culture plates were imaged at defined intervals each day of incubation, but the processing of the cultures (i.e., pathogen identification and antimicrobial susceptibility testing) was only performed during day shift hours (~8:00 A.M. to 4:30 P.M.). The median TAT for negative reports decreased by almost half for urine samples from 52.1 (2017) to 28.3 h (2019) (p < 0.001), and for MRSA screening specimens from 50.7 to 26.3 h (p < 0.001). The difference in median TAT for negative reports was less pronounced for screening of ESBL (50.2 vs. 43.0 h) (p < 0.001) and VRE (50.6 vs. 45.7 h) (p < 0.001). Despite a trend toward shorter result delivery for positive samples, there was no significant change in the median TAT. These results suggest that TAT for negative samples immediately benefit from automation, whereas TAT for positive samples also depend on the laboratory hours of operation and daily human resource management.

Implementation of the WASPLab™ and first year achievements within a university hospital

In essence, automation can be driven by several of the following incentives: increased processing capacity of the laboratory, better costs control through processes standardization, optimized traceability, or improved workflows to reduce turnaround times (TAT). This project aims at presenting an overview of the project management and change management with a focus on the major challenges addressed by lab staff and laboratory leadership during the different phases of the implementation of the WASPLab™ in a routine clinical bacteriology laboratory. This paper reports our experience and reviews changes in the bacteriology laboratory at Geneva University Hospitals when shifting to the WASPLab™. Practically, the whole automation process was segmented into different packages (specimen type-based segmentation) allowing sequential validation, staff training, and routine implementation. Such process allowed reaching 90% of the identified "automatable" samples within 1 year, including personal training, documentation for accreditation supported by publications, without interrupting routine operations. In addition, we implemented a validated automated solution for antimicrobial disk diffusion susceptibility testing. Structured supervision and accurate monitoring of all the activities related to the automation project including key partners such as IT support, technical committee, and after-sales service guaranteed a swift and timely achievement of the project allowing the improvement of the workflow in routine bacteriology within 1 year.

Comparison of the Copan WASPLab incorporating the BioRad expert system against the SIRscan 2000 automatic for routine antimicrobial disc diffusion susceptibility testing

OBJECTIVES

This study investigated the agreement at the categorical level between the Copan WASPLab incorporating the BioRad expert system against the SIRscan 2000 automatic for antimicrobial disc diffusion susceptibility testing.

METHODS

The 338 clinical strains (67 Pseudomonas aeruginosa, 19 methicillin-resistant Staphylococcus aureus, 75 methicillin-sensitive S. aureus and 177 Enterobacterales isolates) analysed in this study were non-duplicate isolates obtained from consecutive clinical samples referred to the clinical bacteriology laboratory at Geneva University Hospitals between June and August 2019. For the WASPLab the inoculum suspension was prepared in strict accordance with the manufacturer's instruction (Copan WASP srl, Brescia, Italy) by adding 2 mL of the 0.5 McFarland primary suspension used for the SIRscan analysis into a sterile tube filled with 4 mL of sterile saline (1:3 dilution). The inoculum (2 × 30 μL loop/spreader) was spread over the entire surface of Mueller-Hinton agar plates according to the AST streaking pattern defined by Copan. The antibiotic discs were dispensed by the WASP and inoculated media were loaded on conveyors for transfer to the automatic incubators. The plates were incubated for 16 h, and several digital images were acquired. Inhibition zone diameters were automatically read by the WASPLab and were adjusted manually whenever necessary. For the SIRscan 2000 automatic, the antimicrobial disc diffusion susceptibility testing was performed according to the EUCAST guidelines. The gradient strip method was used to resolve discrepancies.

RESULTS

The overall categorical agreement between the compared methods reached 99.1% (797/804; 95% CI 98.2%-99.6%), 99.5% (1029/1034; 95% CI 98.9%-99.8%), and 98.8% (2798/2832; 95% CI 98.3%-99.1%) for P. aeruginosa, S. aureus and the Enterobacterales, respectively.

CONCLUSIONS

WASPLab incorporating the BioRad expert system provides a fully automated solution for antimicrobial disc diffusion susceptibility testing with equal or better accuracy than other available phenotypic methods.