Efficient automated semi-quantitative urine culture analysis via BD Urine Culture App

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.

Rapid and automated screening of carbapenemase- and ESBL-producing Gram-negative bacteria from rectal swabs using chromogenic agar media and the ScanStation device

The ScanSation 100 device (Interscience, France) is an incubator allowing real-time detection of bacterial colony growth by frequently imaging agar plates over time, counting CFU, and detecting colony color. This study evaluated its performance for the early detection of carbapenemase-producing bacteria (CPB) and extended-spectrum β-Lactamase-producing bacteria (ESBL-PB) from rectal swabs inoculated on CHROMagar mSuperCARBA and ESBL media, respectively. Rectal screening ESwabs collected from patients admitted to Grenoble University Hospital between January and June 2021 were analyzed. After inoculation, chromogenic media were incubated for 24 h in the automaton, with image acquisition every 30 min. ScanStation results were compared to visual observations of the plates after 24 h of incubation. In total, 501 rectal swabs were tested. ScanStation showed 100% positive percent agreement (PPA) for the detection of CPB and ESBL-PB, whereas the PPA of color categorization ranged between 45% and 100%. Negative percent agreement (NPA) ranged between 70% and 98%. Negative predictive values (NPVs) were 100% for both bacterial groups, whereas positive predictive values (PPVs) were 70.3% for CPB and 81.0% for ESBL-PB. Importantly, real-time screening allowed detection of the first suspected colony within 10-14 h of growth, on average, whereas visual observation is usually only performed once a day after 18-24 h of incubation. Our study demonstrates the potential use of early images to accelerate the detection of CPB and ESBL-PB and implement effective and timely infection control measures. IMPORTANCE The ScanStation 100 device is an incubator able to follow the real-time growth of bacterial colonies on agar plates through digital imaging, allowing users to sort plates according to the presence or absence of colonies, and to distinguish their color using four numeric color filters. Real-time screening shows that first colony detection is possible much earlier (after 10-14 h of growth, on average), whereas visual observation is usually performed only once a day after 18-24 h of incubation. The ScanStation device, combined with chromogenic agar media, is an efficient automated screening method to accelerate the detection of Gram-negative multidrug-resistant bacteria in laboratories that do not have access to larger laboratory automation systems. Our study shows that setting the image acquisition to one or two early images may allow for the detection of positive samples that were inoculated in the morning, by the end of the working day.

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.

Comparison of the APAS Independence Automated Plate Reader System with the Manual Standard of Care for Processing Urine Culture Specimens

Urine cultures are among the highest-volume tests in clinical microbiology laboratories and usually require considerable manual labor to perform. We evaluated the APAS Independence automated plate reader system and compared it to our manual standard of care (SOC) for processing urine cultures. The APAS device provides automated image interpretation of urine culture plate growth and sorts those images that require further evaluation. We examined 1,519 specimens over a 4-month period and compared the APAS growth interpretations to our SOC. We found that 72 of the 1,519 total specimens (4.74%) had growth discrepancies, where these specimens were interpreted differently by the APAS and the technologist, which required additional evaluation of plate images on the APAS system. Overall, there were 56 discrepancies in pathogen identification, which were present in 3.69% of the cultures. An additional pathogen was uncovered in a majority of these discrepancies; 12 (21.4%) identified an additional pathogen for the SOC, and 40 (71.4%) identified an additional pathogen for the APAS workflow. We found 214 (2.69%) antimicrobial susceptibility test (AST) discrepancies; 136 (1.71%) minor errors (mEs), 41 (0.52%) major errors (MEs), and 36 (0.45%) very major errors (VMEs). Many of the MEs and VMEs occurred in only a small subset of 13 organisms, suggesting that the specimen may have had different strains of the same pathogens with differing AST results. Given the significant labor required to perform urine cultures, the APAS Independence system has the potential to reduce manual labor while maintaining the identity and AST results of urinary pathogens. IMPORTANCE Urine cultures are among the highest-volume tests performed in clinical microbiology facilities and require considerable manual labor to perform. We compared the results of our manual SOC workflow with that of the APAS Independence system, which provides automated image interpretation and sorting of urine culture plates based on growth. We examined 1,519 urine cultures processed using both workflows and found that only 4.74% had growth pattern discrepancies and 3.69% pathogen identification discrepancies. There was substantial agreement in AST results between workflows, with only 2.69% having discrepancies. Only 1.71% of the ASTs had mEs, 0.52% had MEs, and 0.45% had VMEs, with most of the MEs and VMEs belonging to a small subset of organisms. The APAS system significantly decreased manual urine culture processing, while providing similar results to the SOC. As such, incorporating such automation into laboratory workflows has the potential to significantly improve efficiency.

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.