Artificial Intelligence Augmented Diagnostics In Clinical Microbiology: A Multimodal Evaluation Of Diagnostic Accuracy, Workflow Efficiency, And Antimicrobial Resistance Prediction.

Artificial intelligence and spine: rise of the machines

Bloodstream infections (BSI) are associated with high mortality rates, especially in immunocompromised patients. Identifying pathogens early and selecting appropriate antimicrobials to treat BSI is integral in reducing the mortality rate. There is a need to reduce the turnaround time (TAT) of pathogen identification as well as to accelerate the antimicrobial susceptibility testing (AST) of blood cultures, which can be achieved by following relevant identification methods and performing the direct AST (DAST) by the disk diffusion method. In this study, blood samples were collected from patients with suspected bacteremia/septicemia, and aseptic precautions were taken to prevent contamination. Samples containing gram-negative bacilli (GNB) were then analyzed by DAST and conventional AST (CAST). We tested 118 GNB-positive isolates in total to compare the results of DAST and CAST. DAST and CAST showed good categorical agreement (CA) for various groups of microorganisms: 98.9% and 99.6% for Enterobacterales and Pseudomonas spp., respectively. Early detection of pathogens in blood along with the determination of their antibiotic susceptibility patterns is a need of the hour. By performing DAST on positive blood culture broth, clinical teams can obtain the information necessary for switching from empirical therapy to definitive treatment one day faster. This rapid identification of the pathogen, along with corresponding AST results, will help clinicians to accelerate targeted antimicrobial therapy for critical patients and, thus, reduce mortality and morbidity rates in patients with bloodstream infections.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in conservative dentistry and endodontics, revolutionizing diagnostic accuracy, treatment planning, and procedural efficiency. This narrative review explores the applications, methodologies, advantages, and challenges of AI in these fields. AI-driven systems, such as convolutional neural networks (CNNs), excel in analyzing dental imaging, including radiographs and cone-beam computed tomography, to detect caries, periapical lesions, and root canal morphologies with high precision. These technologies streamline tasks such as tooth shade determination and working length measurement, reducing human error and enhancing clinical outcomes. Predictive models utilize patient data to assess the risks of caries progression and endodontic complications, thereby enabling the development of personalized treatment plans. Natural language processing aids in extracting insights from clinical records, while generative adversarial networks enhance dataset quality by creating synthetic images. Despite these advancements, challenges persist, including limited availability of diverse, annotated datasets, which affects model generalizability across populations. The opaque nature of some AI algorithms raises concerns about interpretability, potentially undermining clinician trust. High computational requirements and implementation costs limit accessibility, particularly in resource-constrained settings. Ethical issues, such as patient data privacy and the risk of over-reliance on AI, further complicate adoption. Addressing these barriers requires standardized dental imaging databases, transparent algorithms, and collaboration between dental professionals and data scientists. Future research should focus on improving model explainability, expanding dataset diversity, and integrating AI seamlessly into clinical workflows. By overcoming these challenges, AI and ML hold the potential to become indispensable in conservative dentistry and endodontics, offering precise, efficient, and patient-centered solutions that enhance diagnostic reliability and treatment success, ultimately advancing the quality of dental care. This narrative review aimed to explore the theoretical foundations, historical evolution, and practical applications of AI and ML in conservative dentistry and endodontics, with a focus on their types, methodologies, advantages, and limitations.

A Side-by-Side Comparison of Clinical versus Current Good Manufacturing Practices (cGMP) Microbiology Laboratory Requirements for Sterility Testing of Cellular and Gene Therapy Products

Rapid identification and antimicrobial susceptibility testing are essential for timely bloodstream infection (BSI) management. This study aimed to investigate the performance and turnaround time of multiple rapid diagnostic methods in a microbiology laboratory without 24/7 operation. This study included 236 positive blood culture bottles. Rapid identification methods were assessed with the SepsiTyper kit and the FilmArray blood culture identification 2 (BCID2) panel. Rapid antimicrobial susceptibility testing (AST) methods involved direct AST using the BD Phoenix M50 system and QuantaMatrix direct and rapid antimicrobial susceptibility testing (dRAST) and resistance gene detection with the FilmArray BCID2 panel. Conventional methods were used to compare results. The turnaround time was analyzed from blood culture positivity to preparation initiation and from preparation initiation to result reporting. Both rapid identification methods significantly reduced the turnaround time (~1 day and 19 h) compared to conventional identification. SepsiTyper demonstrated higher species-level accuracy in monomicrobial samples, whereas BCID2 outperformed in polymicrobial cases. Among the rapid AST methods, BCID2 and dRAST enabled result reporting within 24 h of positivity. Preparation delays were >45% of the overall turnaround time. Rapid diagnostics substantially shortened the BSI diagnostic time, even in limited-operation settings. Their clinical utility may be improved through 24/7 laboratory workflows.

Clinical microbiology laboratories and COVID-19: the calm before the storm.

<em>Background and aims</em>: Blood culture (BC) results are essential to guide antimicrobial chemotherapy for patients with sepsis. However, BC is a time-consuming exam, which can take several days. Reducing BCs turn around time (TAT) could impact on multiple outcome parameters and TAT monitoring is an important tool for measurement of microbiology laboratory performance. The aim of this study was to provide an overview of BC TATs among Italian microbiology laboratories. <br /><em>Materials and methods</em>: Five laboratories collected and recorded, for a month period, date and time of the BC processing events. Cumulative TATs were analysed using the GraphPad software. <br /><em>Results</em>: Participating laboratories reported data from 302 sepsis episodes. The median time from when the BC system produced a positive signal until Gram-stain results were reported was 7.6 hours. A rapid molecular identification and antimicrobial susceptibility testing (AST) was performed in 26.5% of BCs. Mean TAT for identification report was significantly lower when a molecular approach was adopted (12 vs. 28.7 hours, P<0.001). Similarly, results of the molecular AST were obtained more than 24 hours in advance compared with phenotypic AST (mean 13.2 vs. 47.6, P<0.001). TATs from BC positivity of laboratories opened 7 days/week were not significantly lower than those of laboratories opened 6 days/week. <br /><em>Conclusions</em>: BC is a time-consuming exam, however, molecular identification and AST methods can drastically reduce time to results. The lack of difference between TATs observed for laboratories working 7 days/week and 6 days/week, coupled with a high rate of BCs turning positive during the night enable to conclude that the most urgent measure to reduce TATs is the expansion of laboratory regular duty hours.

A rapid polymerase chain reaction (PCR) method for the direct detection of the staphylococcal mecA gene from BACTEC blood culture bottles (Becton Dickinson, Sparks, MD) was developed. Published primer sequences and sample preparation using Achromopeptidase for cell lysis were adapted to the use of the Idaho Technology Air Thermocycler 1605 (Idaho Technologies, Idaho Falls, ID). The method was validated with 80 strains of coagulase-positive and coagulase-negative geographically diverse methicillin-resistant and susceptible isolates of staphylococci. There was a 100% correlation between the PCR results and the results of standard susceptibility testing methods. From BACTEC 9240 blood cultures, mixed aliquots of blood and broth containing gram-positive cocci in clusters were centrifuged at low speed to sediment red blood cells. After additional centrifugation and wash steps, PCR was performed on the resuspended pellet. The turnaround time from initial Gram stain detection of positive BACTEC bottles to PCR amplicon detection by agarose gel electrophoresis is less than 3 hours. In a clinical evaluation of 181 blood culture isolates, there was a 99% correlation with standard susceptibility results for Staphylococcus aureus. Discrepant results for Staphylococcus aureus isolates were verified by a Mueller Hinton plate supplemented with 6 microg/mL of oxacillin and 2% sodium chloride. For coagulase-negative staphylococci, the PCR method detected an additional seven resistant isolates that were reported by the Vitek as susceptible. Coagulase-negative staphylococcal susceptibility results that were in disagreement with the PCR assay were confirmed by the disk-diffusion method. This procedure is accurate, rapid and fits well into laboratory work flow. Rapid detection of the mecA gene on positive blood culture vials has become a routine test in the authors' clinical microbiology laboratory.

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

In October 2001, the first disseminated biological warfare attack was perpetrated on American soil. Initially, a few clinical microbiology laboratories were testing specimens from acutely ill patients and also being asked to test nasal swabs from the potentially exposed. Soon after, a significant number of clinical microbiology and public health laboratories received similar requests to test the worried well or evaluate potentially contaminated mail or environmental materials, sometimes from their own break rooms. The role of the clinical and public health microbiology laboratory in response to a select agent event or act of bioterrorism is reviewed.

Identification and characterization of micro-organisms that cause infections are crucial for successful management of patients.For several decades, routine clinical microbiological diagnostic laboratories have been equipped with a growing panel of culture-dependent and culture-independent methods to investigate the microbial etiology of infectious diseases.Considering the disadvantages of traditional methods, multiplex PCR techniques have been routinely endorsed in clinical microbiology laboratories as rapid and sensitive diagnostic and prognostic tool including many PCR panels routinely used in microbial diagnostics.The great advance in medical biotechnology has been associated with development of Matrix-Associated Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI TOF MS).The power of MALDI TOF MS correctly identified 93.2% of organisms to the species level and 5.3% to the genus level.Recently, the application of next-generation sequencing (NGS) technology and its various methodological variants makes it possible to detect different types of microorganisms present within a microbial sample simultaneously, using a culture-independent approach and in a single sequencing run.Over the next 5 to 10 years, although it is unlikely to see NGS completely replacing the conventional culture and susceptibility methods, a wealth of NGS applications will be acquired in the vast majority of diagnostic microbiology laboratories worldwide, providing enhanced diagnostic capabilities and improving the quality of patients care.In Egyptian diagnostic microbiology laboratories, we have to ask ourselves; are we ready to subsist this new era?Sure the answer is: 'All things are ready, if our mind be so'.

The rapid rise in increasingly resistant bacteria has become a major threat to public health. Antimicrobial susceptibility testing (AST) is crucial in guiding appropriate therapeutic decisions and infection prevention practices for patient care. However, conventional culture-based AST methods are time-consuming and labor-intensive. Therefore, rapid AST approaches exist to address the delayed gap in time to actionable results. There are two main types of rapid AST technologies- phenotypic and genotypic approaches. In this review, we provide a summary of all commercially available rapid AST platforms for use in clinical microbiology laboratories. We describe the technologies utilized, performance characteristics, acceptable specimen types, types of resistance detected, turnaround times, limitations, and clinical outcomes driven by these rapid tests. We also discuss crucial factors to consider for the implementation of rapid AST technologies in a clinical laboratory and what the future of rapid AST holds.

Introduction Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, and integration challenges in clinical microbiology. Areas covered This review examines AI-driven methodologies, including machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), for enhancing pathogen detection, AMR prediction, and diagnostic imaging. Applications in virology (e.g. COVID-19 RT-PCR optimization), parasitology (e.g. malaria detection), and bacteriology (e.g. automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, and Web of Science (2018–2024), prioritizing peer-reviewed studies on AI’s diagnostic accuracy, workflow efficiency, and clinical validation. Expert opinion AI significantly improves diagnostic precision and operational efficiency but requires robust validation to address data heterogeneity, model interpretability, and ethical concerns. Future success hinges on interdisciplinary collaboration to develop standardized, equitable AI tools tailored for global healthcare settings. Advancing explainable AI and federated learning frameworks will be critical for bridging current implementation gaps and maximizing AI’s potential in combating infectious diseases.

Artificial Intelligence (AI) is revolutionising microbiology by enabling novel approaches to pathogen detection, antimicrobial resistance prediction, genomic analysis, and drug discovery. This visionary review explores the potential of AI to transform microbial research, diagnostics, and public health interventions. AI tools, including machine learning, deep learning, and predictive modelling, facilitate the rapid processing of large-scale microbiological data, enhancing our understanding of microbial diversity, function, and evolution. Current applications include AI-assisted imaging for microbial morphology, genome annotation, metagenomic analysis, and predictive epidemiology. We also discuss challenges, including data quality, algorithm interpretability, ethical considerations, and integration into laboratory workflows. Looking forward, AI promises to accelerate personalised microbiome therapies, real-time outbreak prediction, and high-throughput discovery of novel antimicrobials. This review aims to provide a roadmap for researchers, clinicians, and policymakers to harness AI’s potential, bridging computational intelligence and microbiology to address future challenges in infectious diseases, antimicrobial resistance, and microbial ecology.