Deep Convolutional Neural Networks Implementation for the Analysis of Urine Culture

Smart Nanozymes for Diagnosis of Bacterial Infection: The Next Frontier from Laboratory to Bedside Testing

The global spread of infectious diseases caused by pathogenic bacteria significantly poses public health concerns, and methods for sensitive, selective, and facile diagnosis of bacteria can efficiently prevent deterioration and further spreading of the infections. The advent of nanozymes has broadened the spectrum of alternatives for diagnosing bacterial infections. Compared to natural enzymes, nanozymes exhibit the same enzymatic characteristics but offer greater economic efficiency, enhanced durability, and adjustable dimensions. The importance of early diagnosis of bacterial infection and conventional diagnostic approaches is introduced. Subsequently, the review elucidates the definition, properties, and catalytic mechanism of nanozymes. Eventually, the detailed application of nanozymes in detecting bacteria is explored, highlighting their utilization as biosensors that allow for accelerated and highly sensitive identification of bacterial infections and reflecting on the potential of nanozyme-based bacterial detection as a point-of-care testing (POCT) tool. A brief summary of obstacles and future perspectives in this field is presented at the conclusion of this review.

A Clinical Bacterial Dataset for Deep Learning in Microbiological Rapid On-Site Evaluation

Microbiological Rapid On-Site Evaluation (M-ROSE) is based on smear staining and microscopic observation, providing critical references for the diagnosis and treatment of pulmonary infectious disease. Automatic identification of pathogens is the key to improving the quality and speed of M-ROSE. Recent advancements in deep learning have yielded numerous identification algorithms and datasets. However, most studies focus on artificially cultured bacteria and lack clinical data and algorithms. Therefore, we collected Gram-stained bacteria images from lower respiratory tract specimens of patients with lung infections in Chinese PLA General Hospital obtained by M-ROSE from 2018 to 2022 and desensitized images to produce 1705 images (4,912 × 3,684 pixels). A total of 4,833 cocci and 6,991 bacilli were manually labelled and differentiated into negative and positive. In addition, we applied the detection and segmentation networks for benchmark testing. Data and benchmark algorithms we provided that may benefit the study of automated bacterial identification in clinical specimens.

The Impact of Artificial Intelligence on Microbial Diagnosis

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

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.

Evolution of Diagnostic and Forensic Microbiology in the Era of Artificial Intelligence

Diagnostic microbiology plays a vital role in managing infectious diseases, combating antimicrobial resistance, and containment of outbreaks. During the fourth industrial revolution, when artificial intelligence (AI) became an essential part of our day-to-day lives, its integration into healthcare would further revolutionize our knowledge and potential. Although in the budding stage, AI with machine learning is being increasingly utilized in various aspects of diagnostic microbiology. It can handle large datasets that are difficult to analyze manually. Researchers have developed and demonstrated several machine-learning algorithms for interpreting bacterial cultures, conducting image analysis for microbial detection, and predicting antimicrobial susceptibility patterns. Thus, AI may most likely be the ultimate solution to the ever-increasing demand for improved results with shorter turnaround times. AI can also assist forensic microbiologists in crime scene investigations, as it can guide individual identification, cause and time since death, and manner of death. This review summarizes the application of AI in diagnostic microbiology for performing diverse sets of microbial investigations and is an essential aid in forensic microbiology.

Machine Learning in Laboratory Medicine: Recommendations of the IFCC Working Group

BACKGROUND

Machine learning (ML) has been applied to an increasing number of predictive problems in laboratory medicine, and published work to date suggests that it has tremendous potential for clinical applications. However, a number of groups have noted the potential pitfalls associated with this work, particularly if certain details of the development and validation pipelines are not carefully controlled.

METHODS

To address these pitfalls and other specific challenges when applying machine learning in a laboratory medicine setting, a working group of the International Federation for Clinical Chemistry and Laboratory Medicine was convened to provide a guidance document for this domain.

RESULTS

This manuscript represents consensus recommendations for best practices from that committee, with the goal of improving the quality of developed and published ML models designed for use in clinical laboratories.

CONCLUSIONS

The committee believes that implementation of these best practices will improve the quality and reproducibility of machine learning utilized in laboratory medicine.

SUMMARY

We have provided our consensus assessment of a number of important practices that are required to ensure that valid, reproducible machine learning (ML) models can be applied to address operational and diagnostic questions in the clinical laboratory. These practices span all phases of model development, from problem formulation through predictive implementation. Although it is not possible to exhaustively discuss every potential pitfall in ML workflows, we believe that our current guidelines capture best practices for avoiding the most common and potentially dangerous errors in this important emerging field.

Urinalysis exhibits excellent predictive capacity for the absence of urinary tract infection

OBJECTIVES

To assess predictive value of urinalysis for negative urine culture and absence of urinary tract infection, re-evaluate the microbial growth threshold for positive urine culture result, and describe antimicrobial resistance features. Urine culture is associated with 27% of U.S. hospitalizations, and unnecessary antibiotic prescription is a main antibiotic resistance contributor.

SUBJECTS AND METHODS

Urinalyses with urine culture from women ages 18-49 from 2013 to 2020 were studied. Clinically-diagnosed urinary tract infection (CUTI) was defined as 1) uropathogen growth, 2) documented diagnosis of urinary tract infection, and 3) antibiotic prescription. Sensitivity, specificity, and diagnostic predictive values were used to assess urinalysis performance in predicting isolation of a uropathogen by culture and in detection of CUTI.

RESULTS

12,252 urinalyses were included. 41% of urinalyses were associated with positive urine culture and 1287 (10.5%) with CUTI. Negative urinalysis exhibited high predictive accuracy for negative urine culture (specificity 90.3%, PPV 87.3%) and absence of CUTI (specificity 92.2%, PPV 97.4%). 24% of patients not meeting the CUTI definition were still prescribed antibiotics. 22% of cultures associated with CUTI exhibited growth less than 100,000 CFU/ml. Escherichia coli was implemented as causing 70% of CUTIs, and 4.2% of these produced an extended spectrum beta-lactamase.

CONCLUSIONS

Negative urinalysis exhibits high predictive accuracy for absence of CUTI. A reporting threshold of 10,000 CFU/ml is more clinically appropriate than a 100,000 CFU/ml cutpoint. Reflex culture based on urinalysis results could complement clinical judgement and improve laboratory and antibiotic stewardship in pre-menopausal women.

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.

Catheter-Associated Urinary Tract Infections: Current Challenges and Future Prospects

Catheter-associated urinary tract infection (CAUTI) is the most common healthcare-associated infection and cause of secondary bloodstream infections. Despite many advances in diagnosis, prevention and treatment, CAUTI remains a severe healthcare burden, and antibiotic resistance rates are alarmingly high. In this review, current CAUTI management paradigms and challenges are discussed, followed by future prospects as they relate to the diagnosis, prevention, and treatment. Clinical and translational evidence will be evaluated, as will key basic science studies that underlie preventive and therapeutic approaches. Novel diagnostic strategies and treatment decision aids under development will decrease the time to diagnosis and improve antibiotic accuracy and stewardship. These include several classes of biomarkers often coupled with artificial intelligence algorithms, cell-free DNA, and others. New preventive strategies including catheter coatings and materials, vaccination, and bacterial interference are being developed and investigated. The antibiotic pipeline remains insufficient, and new strategies for the identification of new classes of antibiotics, and rational design of small molecule inhibitor alternatives, are under development for CAUTI treatment.