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.

Design and Implementation of Improved SARS-CoV-2 Diagnostic Assays To Mitigate the Impact of Genomic Mutations on Target Failure: the Xpert Xpress SARS-CoV-2 Experience.

In 2020, the U.S. Food and Drug Administration (FDA) enabled manufacturers to request emergency use authorization (EUA) to facilitate the rapid authorization of in vitro diagnostic (IVD) platforms for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Uncommon SARS-CoV-2 point mutations could cause nucleocapsid (N) gene target failure (NGTF) when using first-generation Xpert Xpress assays, so improvements were designed and implemented. In response to NGTF reports and with consideration of viral genomic information in public databases, the Xpress assays were redesigned to mitigate the impact of SARS-CoV-2 mutations on qualitative assay performance. The second-generation assays include a third gene target (RNA-dependent RNA polymerase [RdRp]) and redundant oligonucleotide probes for the N2 target. First- and second-generation assay performances were evaluated using a challenge set of samples. A second-generation assay with updated oligonucleotide chemistry received FDA EUA in September 2021. A prototype assay with oligonucleotide chemistry similar to that of the second-generation assay with FDA EUA successfully detected all three gene targets (N2, envelope [E], and RdRp) in all challenge samples (100%; 50/50), including variants with N gene mutations (g.29197C>T or g.29200C>T), which caused NGTF in the first-generation assays. Investigation and reporting of IVD target failures, public sharing of viral genomic sequence data, and the FDA EUA pathway were essential components in facilitating a short cycle time from the identification of mutations that impact the performance of an IVD assay to the design and implementation of an improved IVD assay. IMPORTANCE The SARS-CoV-2 genome has mutated during the coronavirus disease 2019 (COVID-19) pandemic. Some of these mutations have impacted the performance of nucleic acid amplification tests like PCR, which are commonly used as diagnostic tools to detect an infection. The U.S. Food and Drug Administration (FDA) emergency use authorization (EUA) process enables the rapid reformulation and regulatory authorization of improved PCRs. In our experience, the identification of SARS-CoV-2 mutations that impact PCR performance, the subsequent development of improved PCR chemistry, and the use of the FDA EUA regulatory pathway led to improved diagnostic performance during the SARS-CoV-2 pandemic that is able to keep pace with the rapidly evolving genome of SARS-CoV-2.

Meningococcal Urethritis: Old and New.

Neisseria meningitidis is a common commensal bacterium found in the respiratory tract, but it can also cause severe, invasive disease. Vaccines have been employed which have been successful in helping to prevent invasive disease caused by encapsulated N. meningitidis from the A, C, W, Y, and B serogroups. Currently, nonencapsulated N. meningitidis groups are more common commensals in the population than in the prevaccine era. One emerging nonencapsulated group of bacteria is the U.S. N. meningitidis urethritis clade (US_NmUC), which can cause meningococcal urethritis in men. US_NmUC has unique genotypic and phenotypic features that may increase its fitness in the male urethra. It is diagnostically challenging to identify and distinguish meningococcal urethritis from Neisseria gonorrhoeae, as the clinical presentation and microbiological findings are overlapping. In this review, the history of meningococcal urethritis, emergence of US_NmUC, laboratory diagnosis, and clinical treatment are all explored.

Digital image analysis of high-grade urothelial carcinoma in urine cytology confirms chromasia heterogeneity and reveals a subset with hypochromatic nuclei and another with extremely dark or "India ink" nuclei.

BACKGROUND: The Paris System for Reporting Urinary Cytology (TPS) uses hyperchromasia as major diagnostic criterion for high-grade urothelial carcinoma (HGUC). The purpose of the study was to evaluate cases that were diagnosed as HGUC by TPS and determine whether there are different chromatin distribution patterns (ie, subsets). METHODS: Digital image annotations were performed on microscopic images of HGUC urine specimens with surgical biopsy/resection follow-up. Median gray values were generated for each cell. Neutrophils (polymorphonuclear leukocyte [PMN]) were also enumerated in each case to serve as an internal control. A HGUC/PMN ratio was generated for each case, and the cases were distributed. RESULTS: Sixty-nine HGUC cases yielded 2660 cells, including 2078 HGUC (30.1 cells/case) and 582 PMNs (8.4 cells/case). The average median gray value of an HGUC was 50.6 and of a PMN was 36.8 (P < .0001). Eight of 69 cases (11.6%) contained nuclei that, on average, were darker than or as dark as a PMN (extremely dark, ie, "India ink"). Fifty-one of 69 cases (74.0%) contained nuclei that, on average, were slightly brighter than a PMN (hyperchromatic). Ten of 69 cases (14.5%) contained nuclei that, on average, were much brighter than a PMN (hypochromatic). Within a single case, all cases showed heterogeneity with the hypochromatic cases showing the most dramatic effect. CONCLUSIONS: Digital image analysis reveals that there are large variations in chromasia between cases including a subset of cases with hypochromasia and another with extremely dark or "India ink" nuclei. There was much heterogeneity of chromasia seen within a single sample.