Automatic Digital Analysis of Chromogenic Media for Vancomycin-Resistant-Enterococcus Screens Using Copan WASPLab

Artificial intelligence in the clinical laboratory

Laboratory medicine has become a highly automated medical discipline. Nowadays, artificial intelligence (AI) applied to laboratory medicine is also gaining more and more attention, which can optimize the entire laboratory workflow and even revolutionize laboratory medicine in the future. However, only a few commercially available AI models are currently approved for use in clinical laboratories and have drawbacks such as high cost, lack of accuracy, and the need for manual review of model results. Furthermore, there are a limited number of literature reviews that comprehensively address the research status, challenges, and future opportunities of AI applications in laboratory medicine. Our article begins with a brief introduction to AI and some of its subsets, then reviews some AI models that are currently being used in clinical laboratories or that have been described in emerging studies, and explains the existing challenges associated with their application and possible solutions, finally provides insights into the future opportunities of the field. We highlight the current status of implementation and potential applications of AI models in different stages of the clinical testing process.

Automated detection of methicillin-resistant Staphylococcus aureus with the MRSA CHROM imaging application on BD Kiestra Total Lab Automation System

The virulence of methicillin-resistant Staphylococcus aureus (MRSA) and its potentially fatal outcome necessitate rapid and accurate detection of patients colonized with MRSA in healthcare settings. Using the BD Kiestra Total Lab Automation (TLA) System in conjunction with the MRSA Application (MRSA App), an imaging application that uses artificial intelligence to interpret colorimetric information (mauve-colored colonies) indicative of MRSA pathogen presence on CHROMagar chromogenic media, anterior nares specimens from three sites were evaluated for the presence of mauve-colored colonies. Results obtained with the MRSA App were compared to manual reading of agar plate images by proficient laboratory technologists. Of 1,593 specimens evaluated, 1,545 (96.98%) were concordant between MRSA App and laboratory technologist reading for the detection of MRSA growth [sensitivity 98.15% (95% CI, 96.03, 99.32) and specificity 96.69% (95% CI, 95.55, 97.60)]. This multi-site study is the first evaluation of the MRSA App in conjunction with the BD Kiestra TLA System. Using the MRSA App, our results showed 98.15% sensitivity and 96.69% specificity for the detection of MRSA from anterior nares specimens. The MRSA App, used in conjunction with laboratory automation, provides an opportunity to improve laboratory efficiency by reducing laboratory technologists' labor associated with the review and interpretation of cultures.

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.

Evaluation of PhenoMATRIX and PhenoMATRIX PLUS for the screening of MRSA from nasal and inguinal/perineal swabs using chromogenic media

The objective of this study was to assess the clinical performances of PhenoMATRIX and PhenoMATRIX PLUS for the screening of methicillin-resistant Staphylococcus aureus (MRSA) from nasal and inguinal/perineal ESwabs using chromogenic media. The automated performances were compared to the manual reading. Additionally, we evaluated PhenoMATRIX PLUS for the automatic release of the negative results to the Laboratory Information System (LIS) and the automatic discharge of the negative plates from the incubators. A total of 6,771 non-duplicate specimens were used by PhenoMATRIX as a machine learning model. The validation of these settings was performed on 17,223 non-duplicate specimens. The MRSA positivity rate was 5% (866/17,223). Validated settings were then used by PhenoMATRIX PLUS on another 1,409 non-duplicate specimens. The sensitivities of PhenoMATRIX and PhenoMATRIX PLUS were 99.8% [95% confidence interval (CI), 99.2%-99.9%] and 100% (95% CI, 92.1%-100%), respectively. The specificities of PhenoMATRIX and PhenoMATRIX PLUS were 99.1% (95% CI, 99.0%-99.2%) and 95.2% (95% CI, 93.8%-96.1%), respectively. All the 1,297 MRSA-negative specimens analyzed by PhenoMATRIX PLUS were automatically released and sent to the LIS immediately after availability of the culture image on the WASPLab (100% accuracy). All negative media plates were automatically discarded. PhenoMATRIX PLUS decreases the time spent by technologists on negative plates and ensures optimal usage of the incubators' capacity.

Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology

Full Laboratory Automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony. Working on a large stream of clinical data and a complete set of 32 pathogens, the proposed system is capable of effectively assist plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of Urinary Tract Infections. Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.

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.

Laboratory automation, informatics, and artificial intelligence: current and future perspectives in clinical microbiology

Clinical diagnostic laboratories produce one product-information-and for this to be valuable, the information must be clinically relevant, accurate, and timely. Although diagnostic information can clearly improve patient outcomes and decrease healthcare costs, technological challenges and laboratory workflow practices affect the timeliness and clinical value of diagnostics. This article will examine how prioritizing laboratory practices in a patient-oriented approach can be used to optimize technology advances for improved patient care.

Microbiology 2.0–A “behind the scenes” consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories

Laboratory automation with Artificial Intelligence (AI) features have now emerged into routine diagnostic clinical use to interpret growth on agar plates. Applications are currently limited to urine samples and infection control screens, yet some of the details around the development of algorithms remain entrenched with AI development specialists and are not well understood by laboratorians. The generation of algorithms is not a trivial task and is a highly structured process, with several considerations needed to develop the appropriate data for specific intended uses. Understanding these considerations highlights the limitations of any algorithm created and informs better design practices so that algorithm objectives can be thoroughly tested prior to routine use.

Metagenomics and artificial intelligence in the context of human health

Human microbiome is ubiquitous, dynamic, and site-specific consortia of microbial communities. The pathogenic nature of microorganisms within human tissues has led to an increase in microbial studies. Characterization of genera, like Streptococcus, Cutibacterium, Staphylococcus, Bifidobacterium, Lactococcus and Lactobacillus through culture-dependent and culture-independent techniques has been reported. However, due to the unique environment within human tissues, it is difficult to culture these microorganisms making their molecular studies strenuous. MGs offer a gateway to explore and characterize hidden microbial communities through a culture-independent mode by direct DNA isolation. By function and sequence-based MGs, Scientists can explore the mechanistic details of numerous microbes and their interaction with the niche. Since the data generated from MGs studies is highly complex and multi-dimensional, it requires accurate analytical tools to evaluate and interpret the data. Artificial intelligence (AI) provides the luxury to automatically learn the data dimensionality and ease its complexity that makes the disease diagnosis and disease response easy, accurate and timely. This review provides insight into the human microbiota and its exploration and expansion through MG studies. The review elucidates the significance of MGs in studying the changing microbiota during disease conditions besides highlighting the role of AI in computational analysis of MG data.

Total Laboratory Automation for Rapid Detection and Identification of Microorganisms and Their Antimicrobial Resistance Profiles

At a time when diagnostic bacteriological testing procedures have become more complex and their associated costs are steadily increasing, the expected benefits of Total laboratory automation (TLA) cannot just be a simple transposition of the traditional manual procedures used to process clinical specimens. In contrast, automation should drive a fundamental change in the laboratory workflow and prompt users to reconsider all the approaches currently used in the diagnostic work-up including the accurate identification of pathogens and the antimicrobial susceptibility testing methods. This review describes the impact of TLA in the laboratory efficiency improvement, as well as a new fully automated solution for AST by disk diffusion testing, and summarizes the evidence that implementing these methods can impact clinical outcomes.

Current State of Laboratory Automation in Clinical Microbiology Laboratory

BACKGROUND

Although it has been 30 years since the first automation systems were introduced in the microbiology laboratory, total laboratory automation (TLA) has only recently been recognized as a valuable component of the laboratory. A growing number of publications illustrate the potential impact of automation. TLA can improve standardization, increase laboratory efficiency, increase workplace safety, and reduce long-term costs.

CONTENT

This review provides a preview of the current state of automation in clinical microbiology and covers the main developments during the last years. We describe the available hardware systems (that range from single function devices to multifunction workstations) and the challenging alterations on workflow and organization of the laboratory that have to be implemented to optimize automation.

SUMMARY

Despite the many advantages in efficiency, productivity, and timeliness that automation offers, it is not without new and unique challenges. For every advantage that laboratory automation provides, there are similar challenges that a laboratory must face. Change management strategies should be used to lead to a successful implementation. TLA represents, moreover, a substantial initial investment. Nevertheless, if properly approached, there are a number of important benefits that can be achieved through implementation of automation in the clinical microbiology laboratory. Future developments in the field of automation will likely focus on image analysis and artificial intelligence improvements. Patient care, however, should remain the epicenter of all future directions and there will always be a need for clinical microbiology expertise to interpret the complex clinical and laboratory information.

Comparison of an Automated Plate Assessment System (APAS Independence) and Artificial Intelligence (AI) to Manual Plate Reading of Methicillin-resistant and Methicillin-susceptible Staphylococcus aureus Chromagar Surveillance Cultures

The automated plate assessment system (APAS Independence; Clever Culture System, Bäch, Switzerland) is an automated imaging station linked with interpretive software that detects target colonies of interest on chromogenic media and sorts samples as negative or presumptive positive. We evaluated the accuracy of the APAS to triage methicillin-resistant Staphylococcus aureus (MRSA) and S. aureus cultures using chromogenic medium compared to that by human interpretation. Patient samples collected from the nares on ESwabs were plated onto BD BBL CHROMagar MRSA II and BD BBL CHROMagar Staph aureus and allowed to incubate for 20 to 24 h at 37°C in a non-CO₂ incubator. Mauve colonies are suggestive of S. aureus and were confirmed with latex agglutination. Following incubation, samples were first interrogated by APAS before being read by a trained technologist blinded to the APAS interpretation. The triaging by both APAS and the technologists was compared for accuracy. Any discordant results required further analysis by a third reader. Over a 5-month period, 5,913 CHROMagar MRSA cultures were evaluated. Of those, 236 were read as concordantly positive, 5,525 were read as concordantly negative, and 152 required discordant analysis. Positive and negative percent agreements (PPA and NPA, respectively) were 100% and 97.3%, respectively. The APAS detected 5 positive cultures that were missed by manual reading and determined to be true positives. In a separate analysis, 744 CHROMagar Staph aureus plates were read in parallel. Of these, 133 were concordantly positive, 585 were concordantly negative, and 26 required discordant analysis. PPA and NPA were 95.7% and 96.7%, respectively. This study confirmed the high sensitivity of digital image analysis by the APAS Independence such that negative cultures can be reliably reported without technologist intervention (negative predictive values [NPVs] of 100% for CHROMagar MRSA and 99.0% for CHROMagar Staph aureus). Triaging using the APAS Independence may provide great efficiency in a laboratory with high throughput of MRSA and S. aureus surveillance cultures.

Laboratory Automation in the Microbiology Laboratory: an Ongoing Journey, Not a Tale?

Clinical chemistry laboratories implemented fully automated devices decades before microbiologists started their subtle approaches to follow. Meanwhile several papers have been published about reduced time to reports, faster workflows, and increased sensitivity as results of lab automation. While the journey of automating microbiology workflows step by step was fascinating and beneficial, monetary aspects were uncommon in most publications. In this issue of the Journal of Clinical Microbiology, K. Culbreath, H. Piwonka, J. Korver, and M. Noorbakhsh (J Clin Microbiol 59:e01969-20, https://doi.org/10.1128/JCM.01969-20) calculate the benefits of total lab automation in terms of cost savings and lab efficiency in a "tale of four laboratories." The authors here provide facts and solid calculations about the benefits achieved in four different-sized labs after implementation of full laboratory automation.

Benefits Derived from Full Laboratory Automation in Microbiology: a Tale of Four Laboratories

Automation in clinical microbiology is starting to become more commonplace and reportedly offers several advantages over the manual laboratory. Most studies have reported on the rapid turnaround times for culture results, including times for identification of pathogens and their respective antimicrobial susceptibilities, but few have studied the benefits from a laboratory efficiency point of view. This is the first large, multicenter study in North America to report on the benefits derived from automation measured in full-time equivalents (FTE), FTE reallocation, productivity, cost per specimen, and cost avoidance. Pre- and post-full automation audits were done at 4 laboratories that have vastly different culture volumes, and results show that regardless of the size of the facility, improved efficiencies can be realized after implementation of full laboratory automation.

Digital Image Analysis for the Detection of Group B Streptococcus from ChromID Strepto B Medium Using PhenoMatrix Algorithms

Group B Streptococcus (GBS) can be found to colonize about 25% of all healthy, adult women and is the leading infectious cause of early neonatal morbidity and mortality in the United States. This study evaluated the clinical performance of PhenoMatrix (PM) chromogenic detection module (CDM) digital imaging software in detection of GBS from LIM broth plated on ChromID Strepto B chromogenic medium (ChromID) using the WASP automated processor. The performance of the PM CDM was compared to manual culture review of the digital images and molecular detection of GBS.

Group B Streptococcus (GBS) can be found to colonize about 25% of all healthy, adult women and is the leading infectious cause of early neonatal morbidity and mortality in the United States. This study evaluated the clinical performance of PhenoMatrix (PM) chromogenic detection module (CDM) digital imaging software in detection of GBS from LIM broth plated on ChromID Strepto B chromogenic medium (ChromID) using the WASP automated processor. The performance of the PM CDM was compared to manual culture review of the digital images and molecular detection of GBS. ChromID alone had a sensitivity and specificity of 84.5% and 94.7%, respectively, after 48 h compared to nucleic acid amplification testing (NAAT). Compared to the composite reference for positivity, when PM CDM was used to detect GBS from ChromID, the sensitivity was 100%, with no true-positive GBS isolates missed by 48 h of incubation. Overall, evaluating all three methods for the detection of GBS, the sensitivities of NAAT, ChromID plus PM CDM at 48 h, and ChromID alone at 48 h were 96.8%, 95.5%, and 90.3%, respectively. The specificities of NAAT, ChromID plus PM CDM, and ChromID alone were 97.7%, 63.0%, and 95.4%, respectively. The sensitivity of ChromID in combination with the PM CDM was similar to the sensitivity of molecular detection. Further, the algorithm never called a culture negative that was determined to be positive by manual reading, and it identified an additional eight true positive specimens that were missed by manual digital image culture reading.

Use of artificial intelligence for tailored routine urine analyses

OBJECTIVES

Urine is the most common material tested in clinical microbiology laboratories. Automated analysis is already performed, permitting quicker results and decreasing the laboratory technologist's (LT) workload. These automatic systems have introduced digital imaging concepts. PhenoMATRIX (PHM) is an artificial intelligence software that merges picture algorithms and user rules to provide presumptive results. This study aimed at designing a tailored workflow using PHM, performing its validation and checking its performance in routine practice.

METHODS

Two data collections including 96 and 135 urine samples from nephrostomy/ureterostomy and artificial bladder (US), 948 and 1257 urine samples from catheter (UC) and 3251 and 2027 midstream urine (MSU) were used to compare LT results with those obtained using two versions of PHM. Another 19 US, 102 UC and 508 MSU were used to monitor performance level 3 months after routine implementation.

RESULTS

Before and after revisions, agreement between the first version of PHM and LT results were 83% (95% confidence interval [CI], 74.3-90.2) and 83% (95% CI, 75.3-90.9) (US), 66.7% (95% CI, 63.5-69.5) and 71.7% (95% CI, 68.8-74.4) (UC) and 65.4% (95% CI, 63.8-67.1) and 76% (95% CI, 74.1-77.1) (MSU). The second version improved results, demonstrating 96.2% (95% CI, 91.6-98.8) and 97% (95% CI, 92.6-99.2) (US), 87.5% (95% CI, 85.5-89.2) and 88.9% (95% CI, 87.0-90.5) (UC) and 91% (95% CI, 89.7-92.1) and 92% (95% CI, 91.1-93.4) (MSU) of agreement with LT results before and after revisions. The reliability of PHM results was confirmed by a routine study demonstrating 92% (95% CI, 90.0-94.2) overall agreement.

CONCLUSIONS

PHM showed high performance, with >90% of results in agreement with LT. PHM could help standardize and secure results, prioritize positive plates during analytical workflow and likely save LT time.

Performance evaluation of the Becton Dickinson Kiestra™ IdentifA/SusceptA

OBJECTIVES

New automated modules are required to provide fully automated solutions in diagnostic microbiology laboratories. We evaluated the performance of a Becton Dickinson Kiestra™ IdentifA/SusceptA prototype for MALDI-TOF identification (ID) and Phoenix™ antibiotic susceptibility testing (AST).

METHODS

The performance of the IdentifA/SusceptA coupled prototype was compared with manual processing for MALDI-TOF ID on 1302 clinical microbial isolates or ATCC strains and for Phoenix™ M50 AST on 484 strains, representing 61 species.

RESULTS

Overall, the IdentifA exhibited similar ID performances than manual spotting. Higher performances were observed for Gram-negative bacteria with an ID at the species level (score >2) of 96.5% (369/382) and 86.9% (334/384), respectively. A significantly better performance was observed with the IdentifA (95.2%, 81/85) compared with manual spotting (75.2%, 64/85) from colonies on MacConkey agar. Contrariwise, the IdentifA exhibited lower ID performances at the species level than manual processing for streptococci (76.1%, 96/126 compared with 92%, 115/125), coagulase-negative staphylococci (73.3%, 44/60 compared with 90%, 54/60) and yeasts (41.3%, 19/46 compared with 78.2%, 36/46). Staphylococcus aureus and enterococci were similarly identified by the two approaches, with ID rates of 92% (65/70) for the IdentifA and 92.7%, (64/69) for manual processing and 94.8%, (55/58) for the IdentifA and 98.2%, (57/58) for manual processing, respectively. The SusceptA exhibited an AST overall essential agreement of 98.82% (6863/6945), a category agreement of 98.86% (6866/6945), 1.05% (6/570) very major errors, 0.16% (10/6290) major errors, and 0.91% (63/6945) minor errors compared to the reference AST.

CONCLUSIONS

Overall, the automated IdentifA/SusceptA exhibited high ID and AST performances.

Clinical evaluation of the APAS® Independence: Automated imaging and interpretation of urine cultures using artificial intelligence with composite reference standard discrepant resolution

BACKGROUND

This study reports the outcome of the first evaluation of the APAS® Independence for automated reading and preliminary interpretation of urine cultures in the routine clinical microbiology laboratory. In a 2-stage evaluation involving 3000 urine samples, two objectives were assessed; 1) the sensitivity and specificity of the APAS® Independence compared to microbiologists using colony enumeration as the primary determinant, and 2) the variability between microbiologists in enumerating bacterial cultures using traditional culture reading techniques, performed independently to APAS® Independence interpretation.

METHODS

Routine urine samples received into the laboratory were processed and culture plates were interpreted by standard methodology and with the APAS® Independence. Results were compared using typical discrepant result resolution and with a composite reference standard, which provided an alternative assessment of performance.

RESULTS

The significant growth sensitivity of the APAS® Independence was determined to be 0.919 with a 95% confidence interval of (0.879, 0.948), and the growth specificity was 0.877 with a 95% confidence interval of (0.827, 0.916). Variability between microbiologists was demonstrated with microbiologist bi-plate enumerations in agreement with the consensus 88.6% of the time.

CONCLUSION

The APAS® Independence appears to offer microbiology laboratories a mechanism to standardise the processing and assessment of urine cultures whilst augmenting the skills of specialist microbiology staff.

Computer Vision and Artificial Intelligence Are Emerging Diagnostic Tools for the Clinical Microbiologist

Artificial intelligence (AI) is increasingly becoming an important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these solutions have the potential to improve a test's turnaround time, quality, and cost. A study by Mathison et al. used computer vision AI (B. A. Mathison, J. L. Kohan, J. F. Walker, R. B. Smith, et al., J Clin Microbiol 58:e02053-19, 2020, https://doi.org/10.1128/JCM.02053-19), but additional opportunities for AI applications exist within the clinical microbiology laboratory. Large data sets within clinical microbiology that are amenable to the development of AI diagnostics include genomic information from isolated bacteria, metagenomic microbial findings from primary specimens, mass spectra captured from cultured bacterial isolates, and large digital images, which is the medium that Mathison et al. chose to use. AI in general and computer vision in specific are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology.

Machine Learning Takes Laboratory Automation to the Next Level

Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the Journal of Clinical Microbiology, M. L. Faron, B. W. Buchan, R. F. Relich, J. Clark, and N. A. Ledeboer (J Clin Microbiol 58:e01683-19, 2020, https://doi.org/10.1128/JCM.01683-19) evaluate the performance of automated image analysis software to screen urine cultures for further workup according to their total number of CFU. Urine cultures are the highest volume specimen type for most laboratories, so this software has the potential for tremendous gains in laboratory efficiency and quality due to the consistency of colony quantification.

Evaluation of the WASPLab Segregation Software To Automatically Analyze Urine Cultures Using Routine Blood and MacConkey Agars

Automation of the clinical microbiology laboratory has become more prominent as laboratories face higher specimen volumes and understaffing and are becoming more consolidated. One recent advancement is the use of digital image analysis to rapidly distinguish between chromogenic growth for screening bacterial cultures. In this study, colony segregation software developed by Copan (Brescia, Italy) was evaluated to distinguish between significant growth and no growth of urine cultures plated onto standard blood and MacConkey agars. Specimens from 3 sites were processed on a WASP instrument (Copan) and incubated on the WASPLab platform (Copan), and plates were imaged at 0 and 24 hours postinoculation. Images were read by technologists following validated laboratory protocols (VLPs), and results were recorded in the laboratory information systems (LIS). Image analysis performed colony counts on the 24-hour images, and results were compared with the VLP. A total of 12,931 urine cultures were tested and analyzed with an overall sensitivity and specificity of 99.8% and 72.0%, respectively. After secondary review, 91.1% of manual-positive/automation-negative specimens were due to expert rules that reported the plate as contaminated or growing only normal flora and not due to threshold counts. Nine specimens were found to be manual-positive/automation-negative; a secondary review demonstrated that the results of 8 of these specimens were due to growth of microcolonies that were programmed to be ignored by the software and 1 were due to a colony count near the limit of significance. Overall, the image analysis software proved to be highly sensitive and can be utilized by laboratories to batch-review negative cultures to improve laboratory workflow.

Image analysis and artificial intelligence in infectious disease diagnostics

BACKGROUND

Microbiologists are valued for their time-honed skills in image analysis, including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears and histopathologic slides. They also must classify colony growth on a variety of agar plates for triage and assessment. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses.

OBJECTIVES

To review current AI-based image analysis as applied to clinical microbiology; and to discuss future trends in the field.

SOURCES

Material sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed.

CONTENT

We describe application of machine learning towards analysis of different types of microbiologic image data. Specifically, we outline progress in smear and plate interpretation as well as the potential for AI diagnostic applications in the clinical microbiology laboratory.

IMPLICATIONS

Combined with automation, we predict that AI algorithms will be used in the future to prescreen and preclassify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and the microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.

Clinical microbiology

The clinical microbiology laboratory relies on traditional diagnostic methods such as culturing, Gram stains, and biochemical testing. Receipt of a high-quality specimen with an appropriate test order is integral to accurate testing. Recent technological advancements have led to decreased time to results and improved diagnostic accuracy. Examples of advancements discussed in this chapter include automation of bacterial culture processing and incubation, as well as introduction of mass spectrometry for the proteomic identification of microorganisms. In addition, molecular testing is increasingly common in the clinical laboratory. Commercially available multiplex molecular assays simultaneously test for a broad array of syndromic-related pathogens, providing rapid and sensitive diagnostic results. Molecular advancements have also transformed point-of-care (POC) microbiology testing, and molecular POC assays may largely supplant traditional rapid antigen testing in the future. Integration of new technologies with traditional testing methods has led to improved quality and value in the clinical microbiology laboratory.

After reviewing this chapter, the reader will be able to:•

List key considerations for specimen collection for microbiology testing.

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Discuss the advantages and limitations of automation in the clinical microbiology laboratory.

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Describe the evolution of microorganism identification methods.

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Discuss the benefits and limitations of molecular microbiology point-of-care testing.

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Summarize currently available multiplex molecular microbiology testing options.

Evaluation of WASPLab Software To Automatically Read chromID CPS Elite Agar for Reporting of Urine Cultures

Urine cultures are among the most common specimens received by clinical laboratories and generate a major share of the laboratory workload. Chromogenic agar can expedite culture results, but technologist review is still needed. In this study, we evaluated the ability of the WASPLab software to interpret urine specimens plated onto chromID CPS Elite (CPSE) agar. Urine specimens submitted for bacterial culture were plated onto CPSE agar with a 1-μl loop using the WASP. Each plate was imaged after 0 and 18 h of incubation, and colonies were enumerated by color using the WASPLab software and a technologist's reading from a high-definition (HD) monitor. The results were reported as negative if <10 colonies/plate were detected. Laboratory information system (LIS) time stamps were used to measure the time to result. A total of 1,581 urine cultures were tested. The sensitivity and specificity of the software were 99.8% and 68.5%, respectively, which included 2 manual-positive/automation-negative (MP/AN) results and 170 manual-negative/automation-positive (MN/AP) results. Of the 170 MN/AP specimens, 116 were caused by microcolonies missed by the technologist. The remaining MN/AP results were caused by either count differences near the 10-colony threshold (n = 43) or count differences of >50 CFU (n = 11). The use of both CPSE agar and the WASPLab software improved the time to result for urine culture, reducing the average time to result by 4 h 42 min for negative specimens and 3 h 28 min for positive specimens compared to that with standard-of-care testing. These data demonstrate that the use of CPSE agar and automated plate reading has the potential to improve turnaround time while maintaining high sensitivity and reducing urine culture workload.

Automated Detection of Streptococcus pyogenes Pharyngitis by Use of Colorex Strep A CHROMagar and WASPLab Artificial Intelligence Chromogenic Detection Module Software

Colorex Strep A agar (CHROMagar, Paris, France) was evaluated with PhenoMATRIX chromogenic detection module (CDM) software (Copan Diagnostics Inc., Murrieta, CA) to detect group A Streptococcus (GAS) from throat specimens. The software results were compared to those of manual plate image reading. In addition, GAS PCR testing was performed on all specimens. True-positive specimens were defined as culture-positive (by either PhenoMATRIX CDM or manual reading) specimens confirmed as GAS by matrix-assisted laser desorption ionization-time of flight mass spectrometry plus any culture-negative specimens that were positive by both initial and repeat PCR testing. Of 480 specimens, 96 were considered true-positive specimens. Software reading of the chromogenic agar for suspected colonies detected 110 orange colonies, whereas technologist reading interpreted only 93/110 specimens (84.5%) as positive. None of the 361 cultures interpreted as negative by the PhenoMATRIX CDM software was positive by manual reading. In comparison with true-positive results, the sensitivity and specificity were 96.9% and 100% for PCR testing, 87.5% and 97.7% for technologist reading of chromogenic agar, 90.6% and 94.0% for software reading of chromogenic agar, 83.3% and 97.7% for technologist reading for β-hemolysis on blood agar, and 39.5% and 83.1% for technologist reading for β-hemolysis on blood agar accompanied by any zone of inhibition around a bacitracin-impregnated disk, respectively. The software had the most accurate results of the non-molecular testing methods, detecting all suspected colonies on the chromogenic agar and identifying 3 additional true-positive specimens that were missed by manual reading. The PhenoMATRIX CDM software and the Colorex Strep A agar can improve detection of GAS from throat specimens, and they compared favorably to molecular testing.

Rectal screening for carbapenemase-producing Enterobacteriaceae: a proposed workflow

OBJECTIVES

Active screening is a crucial element for the prevention of carbapenemase-producing Enterobacteriaceae (CPE) transmission in healthcare settings. Here we propose a culture-based protocol for rectal swab CPE screening that combines CPE detection with identification of the carbapenemase type.

METHODS

The workflow integrates an automatic digital analysis of selective chromogenic media (WASPLab®; Copan), with subsequent rapid tests for the confirmation of carbapenemase production [i.e. detection of Klebsiella pneumoniae carbapenemase (KPC)-specific peak by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF/MS) or a multiplex immunochromatographic assay identifying the five commonest carbapenemase types]. To evaluate the performance of this protocol in depth, data for 21 162 rectal swabs submitted for CPE screening to the Microbiology Unit of S. Orsola-Malpighi Hospital (Bologna, Italy) were analysed.

RESULTS

Considering its ability to correctly segregate plates with/without Enterobacteriaceae, WASPLab Image Analysis Software showed globally a sensitivity and specificity of 100% and 79.4%, respectively. Of the plates with bacterial growth (n = 901), 693 (76.9%) were found to be positive for CPE by MALDI-TOF/MS (KPC-specific peak for K. pneumoniae) or by immunochromatographic assay. Only 2.8% (16/570) of KPC-positive K. pneumoniae strains were missed by the specific MALDI-TOF/MS algorithm, being detected by the immunochromatographic assay. The mean turnaround time needed from sample arrival to the final report ranged between 18 and 24 h, representing a significant time saving compared with manual reading.

CONCLUSION

This workflow proved to be fast and reliable, being particularly suitable for areas endemic for KPC-producing K. pneumoniae and for high-throughput laboratories.

Copan WASPLab automation significantly reduces incubation times and allows earlier culture readings

OBJECTIVE

The aim was to evaluate whether laboratory automation (inoculation and automated incubation combined with timely defined high-resolution digital imaging) may help reduce the time required to obtain reliable culture analysis results.

METHODS

We compared the results obtained by WASPLab automation against WASP-based automated inoculation coupled to conventional incubation and manual diagnostic on 1294 clinical samples (483 for the derivation set and 811 for the independent validation set) that included urine, genital tract and non-sterile site specimens, as well as ESwabs for screening of methicillin-resistant Staphylococcus aureus (MRSA), methicillin-sensitive Staphylococcus aureus (MSSA), extended-spectrum beta-lactamases (ESBLs) and carbapenemase-producing Enterobacteriaceae (CPE). We used sequential routine specimens referred to the bacteriology laboratory at Geneva University Hospitals between October 2018 and March 2019.

RESULTS

The detection sensitivity of MRSA and MSSA at 18 hr on WASPLab was 100% (95% confidence interval [CI], 94.48-100.00%). The detection sensitivity of ESBL and CPE at 16 hr on WASPLab was 100% (95% confidence interval [CI], 94.87% to 100.00%). For urine specimens, the similarity was 79% (295/375) between 18 hr and 24 hr of incubation on WASPLab. For genital tract and non-sterile site specimens, the similarity between 16 hr and 28 hr of incubation on WASPLab were 26% (72/281) and 77% (123/159) respectively. Thus, 28 hr was defined as the final incubation time on WASPLab for genital tract and non-sterile site specimens.

CONCLUSIONS

The results of this study show that WASPLab automation enables a reduction of the culture reading time for all specimens tested without affecting performances. Implementing the established and duly validated incubation times will allow appropriate laboratory workflows for improved efficiency to be built.

Clinical Microbiology Is Growing Up: The Total Laboratory Automation Revolution

BACKGROUND

Historically, culture-based microbiology laboratory testing has relied on manual methods, and automated methods (such as those that have revolutionized clinical chemistry and hematology over the past several decades) were largely absent from the clinical microbiology laboratory. However, an increased demand for microbiology testing and standardization of sample-collection devices for microbiology culture, as well as a dwindling supply of microbiology technologists, has driven the adoption of automated methods for culture-based laboratory testing in clinical microbiology.

CONTENT

We describe systems currently enabling total laboratory automation (TLA) for culture-based microbiology testing. We describe the general components of a microbiology automation system and the various functions of these instruments. We then introduce the 2 most widely used systems currently on the market: Becton Dickinson's Kiestra TLA and Copan's WASPLab. We discuss the impact of TLA on metrics such as turnaround time and recovery of microorganisms, providing a review of the current literature and perspectives from laboratory directors, managers, and technical staff. Finally, we provide an outlook for future advances in TLA for microbiology with a focus on artificial intelligence for automated culture interpretation.

SUMMARY

TLA is playing an increasingly important role in clinical microbiology. Although challenges remain, TLA has great potential to affect laboratory efficiency, turnaround time, and the overall quality of culture-based microbiology testing.

Laboratory Automation in Clinical Microbiology

Laboratory automation is currently the main organizational challenge for microbiologists. Automating classic workflows is a strenuous process for the laboratory personnel and a huge and long-lasting financial investment. The investments are rewarded through increases in quality and shortened time to report. However, the benefits for an individual laboratory can only be estimated after the implementation and depending on the classic workflows currently performed. The two main components of automation are hardware and workflow. This review focusses on the workflow aspects of automation and describes some of the main developments during recent years. Additionally, it tries to define some terms which are related to automation and specifies some developments which would further improve automated systems.

Impact of total laboratory automation on workflow and specimen processing time for culture of urine specimens

Total laboratory automation (TLA) has the potential to reduce specimen processing time, improve standardization of cultures, and decrease turnaround time (TAT). The objective of this study was to perform a detailed interrogation of the impact of TLA implementation in all aspects of the workflow for routine culture of urine specimens. Using a detailed motion capture study, the time required for major steps of processing and result reporting were prospectively assessed for urine samples prior to (n = 215) and after (n = 203) implementation of the BD Kiestra TLA system. Specimens were plated on all shifts, but cultures were read only during the day shift for both time periods. Significant increases were noted in the time from receipt to inoculation (23.0 min versus 32.0 min, p < 0.001) and total processing time (28.0 min versus 66.0 min, p < 0.0001) for urine specimens post-TLA. Rates of positive (18.6% versus 16.3%) and negative (71.2% versus 79.3%) urine cultures remained stable through the pre- and post-TLA time periods (p = 0.58). There were no changes in TAT for organism identification or susceptibility results. The time to final report was decreased from 43.8 h pre-TLA to 42.0 h post-TLA, which was attributed to significant decreases in TAT for negative cultures (42.0 h versus 37.5 h, p = 0.01). These findings demonstrate that changes in laboratory workflow are necessary to maximize efficiency of TLA and optimize TAT.

Towards automated detection, semi-quantification and identification of microbial growth in clinical bacteriology: A proof of concept

Background

Automation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, semi-quantification and identification of bacterial colonies.

Methods

Defined monomicrobial and clinical urine samples were inoculated by the BD Kiestra™ InoqulA™ BT module. Image acquisition of plates was performed with the BD Kiestra™ ImagA BT digital imaging module using the BD Kiestra™ Optis™ imaging software. The algorithms were developed and trained using defined data sets and their performance evaluated on both defined and clinical samples.

Results

The detection algorithms exhibited 97.1% sensitivity and 93.6% specificity for microbial growth detection. Moreover, quantification accuracy of 80.2% and of 98.6% when accepting a 1 log tolerance was obtained with both defined monomicrobial and clinical urine samples, despite the presence of multiple species in the clinical samples. Automated identification accuracy of microbial colonies growing on chromogenic agar from defined isolates or clinical urine samples ranged from 98.3% to 99.7%, depending on the bacterial species tested.

Conclusion

The development of intelligent algorithm represents a major innovation that has the potential to significantly increase laboratory quality and productivity while reducing turn-around-times. Further development and validation with larger numbers of defined and clinical samples should be performed before transferring intelligent imaging analysis into diagnostic laboratories.

A Decade of Development of Chromogenic Culture Media for Clinical Microbiology in an Era of Molecular Diagnostics

In the last 25 years, chromogenic culture media have found widespread application in diagnostic clinical microbiology. In the last decade, the range of media available to clinical laboratories has expanded greatly, allowing specific detection of additional pathogens, including Pseudomonas aeruginosa, group B streptococci, Clostridium difficile, Campylobacter spp., and Yersinia enterocolitica. New media have also been developed to screen for pathogens with acquired antimicrobial resistance, including vancomycin-resistant enterococci, carbapenem-resistant Acinetobacter spp., and Enterobacteriaceae with extended-spectrum β-lactamases and carbapenemases. This review seeks to explore the utility of chromogenic media in clinical microbiology, with particular attention given to media that have been commercialized in the last decade. The impact of laboratory automation and complementary technologies such as matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is also assessed. Finally, the review also seeks to demarcate the role of chromogenic media in an era of molecular diagnostics.

Automatic Digital Plate Reading for Surveillance Cultures

The automation of specimen processing and culture workup has rapidly emerged in clinical microbiology laboratories throughout the world and more recently in the United States. While many U.S. laboratories have implemented some form of automated specimen processing and some have begun performing digital plate reading, automated colony analysis is just beginning to be utilized clinically. In this issue of the Journal of Clinical Microbiology, M. L. Faron et al. (J Clin Microbiol 54:2470-2475, 2016, http://dx.doi.org/10.1128/JCM.01040-16) report the results of their evaluation of the performance of the WASPLab Chromogenic Detection Module (CDM) for categorizing chromogenic agar plates as negative or "nonnegative" for vancomycin-resistant enterococci (VRE). Their major finding was 100% sensitivity for detection of "nonnegative" specimens using CDM compared to manual methods for specimens plated on two different types of VRE chromogenic agar plates. Additionally, utilization of digital plate reading in conjunction with automated colony analysis was predicted to result in significant savings based on greatly reduced labor costs.