<p>Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. Recent advancements in the field of TB diagnostics have focused on the identification and utilization of blood-based biomarkers, offering a non-invasive, rapid, and scalable approach to disease detection. This review provides a comprehensive overview of the latest progress in blood-based biomarkers for TB, highlighting their potential to revolutionize diagnostic strategies. Furthermore, we explore emerging technologies such as NGS, PET-CT, Xpert and line probe assays, which have enhanced the sensitivity, specificity, and accessibility of biomarker-based diagnostics. The integration of artificial intelligence (AI) and machine learning (ML) in biomarker analysis is also examined, showcasing its potential to improve diagnostic accuracy and predictive capabilities. This review underscores the need for multidisciplinary collaboration and continued innovation to translate these promising technologies into practical, point-of-care solutions. By addressing these challenges, blood-based biomarkers and emerging technologies hold the potential to significantly improve TB diagnosis, ultimately contributing to global efforts to eradicate this devastating disease.</p>

Digital object identifier (DOI): 10.3389/fcimb.2025.1567592

<p>This study aimed to validate Metasystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module (Neon Metafer) with assistance on respiratory and pleural samples, compared to conventional manual fluorescence microscopy (MM). Analytical parameters were assessed first, followed by a retrospective validation study. In all, 320 archived auramine-O-stained slides selected non-consecutively [85 originally reported as AFB-smear-positive, 235 AFB-smear-negative slides; with an overall mycobacterial culture positivity rate of 24.1% (77/320)] underwent whole-slide imaging and were analyzed by the Metafer Neon AFB Module (version 4.3.130) using a predetermined probability threshold (PT) for AFB detection of 96%. Digital slides were then examined by a trained reviewer blinded to previous AFB smear and culture results, for the final interpretation of assisted digital microscopy (a-DM). Paired results from both microscopic methods were compared to mycobacterial culture. A scanning failure rate of 10.6% (34/320) was observed, leaving 286 slides for analysis. After discrepant analysis, concordance, positive and negative agreements were 95.5% (95%CI, 92.4%-97.6%), 96.2% (95%CI, 89.2%-99.2%), and 95.2% (95%CI, 91.3%-97.7%), respectively. Using mycobacterial culture as reference standard, a-DM and MM had comparable sensitivities: 90.7% (95%CI, 81.7%-96.2%) versus 92.0% (95%CI, 83.4%-97.0%) ( -value = 1.00); while their specificities differed 91.9% (95%CI, 87.4%-95.2%) versus 95.7% (95%CI, 92.1%-98.0%), respectively ( -value = 0.03). Using a PT of 96%, MetaSystems' platform shows acceptable performance. With a national laboratory staff shortage and a local low mycobacterial infection rate, this instrument when combined with culture, can reliably triage-negative AFB-smear respiratory slides and identify positive slides requiring manual confirmation and semi-quantification. This manuscript presents a full validation of MetaSystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module using a probability threshold of 96% including accuracy, precision studies, and evaluation of limit of AFB detection on respiratory samples when the technology is used with assistance. This study is complementary to the conversation started by Tomasello et al. on the use of image analysis artificial intelligence software in routine mycobacterial diagnostic activities within the context of high-throughput laboratories with low incidence of tuberculosis.</p>

Digital object identifier (DOI): 10.1128/jcm.01069-23

<p>The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.</p>

Digital object identifier (DOI): 10.3389/fmicb.2024.1449844

<p>Manual reading of fluorescent acid-fast bacilli (AFB) microscopy slides is time-intensive and technically demanding. The aim of this study was to evaluate the accuracy<br /> of MetaSystems’ automated fluorescent AFB slide scanner and analyzer. Auramine O-stained slides corresponding to 133 culture-positive and 363 culture-negative respiratory (n = 284), tissue (n = 120), body fluid (n = 81), and other (n = 11) sources were evaluated with the MetaSystems Mycobacteria Scanner running the NEON Metafer AFB Module. The sensitivity and specificity of the MetaSystems platform was measured as a standalone diagnostic and as an assistant to technologists to review positive images. Culture results were used as the reference method. The MetaSystems platform failed to scan 57 (11.5%) slides. The MetaSystems platform used as a standalone had a sensitivity of 97.0% (129/133; 95% CI 92.5 to 99.2) and specificity of 12.7% (46/363; 95% CI 9.4 to 16.5). When positive scans were used to assist technologists, the MetaSystems platform had a sensitivity of 70.7% (94/133; 95% CI 62.2 to 78.3) and specificity of 89.0% (323/363; 95% CI 85.3 to 92.0). The manual microscopy method had a sensitivity of 79.7% (106/133; 95% CI 71.9 to 86.2) and specificity of 98.6% (358/363; 95% CI 96.8 to 99.6). The sensitivity of the MetaSystems platform was not impacted by smear grade or mycobacterial species. The majority (70.3%) of false positive smears had $21 smear results with the MetaSystems platform. Further performance improvements are needed before the MetaSystems’ automated fluorescent AFB slide reader can be used to assist microscopist in the clinical laboratory.</p>

Digital object identifier (DOI): 10.1128/jcm.01131-22