Automatic microscopic detection of mycobacteria in sputum: a proof-of-concept

Tuberculosis Diagnosis: Current, Ongoing, and Future Approaches

Tuberculosis (TB) remains an impactful infectious disease, leading to millions of deaths every year. Mycobacterium tuberculosis causes the formation of granulomas, which will determine, through the host-pathogen relationship, if the infection will remain latent or evolve into active disease. Early TB diagnosis is life-saving, especially among immunocompromised individuals, and leads to proper treatment, preventing transmission. This review addresses different approaches to diagnosing TB, from traditional methods such as sputum smear microscopy to more advanced molecular techniques. Integrating these techniques, such as polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP), has significantly improved the sensitivity and specificity of M. tuberculosis identification. Additionally, exploring novel biomarkers and applying artificial intelligence in radiological imaging contribute to more accurate and rapid diagnosis. Furthermore, we discuss the challenges of existing diagnostic methods, including limitations in resource-limited settings and the emergence of drug-resistant strains. While the primary focus of this review is on TB diagnosis, we also briefly explore the challenges and strategies for diagnosing non-tuberculous mycobacteria (NTM). In conclusion, this review provides an overview of the current landscape of TB diagnostics, emphasizing the need for ongoing research and innovation. As the field evolves, it is crucial to ensure that these advancements are accessible and applicable in diverse healthcare settings to effectively combat tuberculosis worldwide.

Pulmonary Tuberculosis Diagnosis Using an Intelligent Microscopy Scanner and Image Recognition Model for Improved Acid-Fast Bacilli Detection in Smears

Microscopic examination of acid-fast mycobacterial bacilli (AFB) in sputum smears remains the most economical and readily available method for laboratory diagnosis of pulmonary tuberculosis (TB). However, this conventional approach is low in sensitivity and labor-intensive. An automated microscopy system incorporating artificial intelligence and machine learning for AFB identification was evaluated. The study was conducted at an infectious disease hospital in Jiangsu Province, China, utilizing an intelligent microscope system. A total of 1000 sputum smears were included in the study, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFBs. Referee technicians served as the gold standard for discrepant results. The automated system demonstrated an overall accuracy of 96.70% (967/1000), sensitivity of 91.94% (194/211), specificity of 97.97% (773/789), and negative predictive value (NPV) of 97.85% (773/790) at a prevalence of 21.1% (211/1000). Incorporating AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Retrospective validation of MetaSystems' deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria

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%) (P-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 (P-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.

IMPORTANCE

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.

Application of the Peroxidase‒like Activity of Nanomaterials for the Detection of Pathogenic Bacteria and Viruses

Infectious diseases caused by pathogenic bacteria and viruses pose a significant threat to human life and well-being. The prompt identification of these pathogens, characterized by speed, accuracy, and efficiency, not only aids in the timely screening of infected individuals and the prevention of further transmission, but also facilitates the precise diagnosis and treatment of patients. Direct smear microscopy, microbial culture, nucleic acid-based polymerase chain reaction (PCR), and enzyme-linked immunosorbent assay (ELISA) based on microbial surface antigens or human serum antibodies, have made substantial contributions to the prevention and management of infectious diseases. Due to its shorter processing time, simple equipment requirements, and no need for professional and technical personnel, ELISA has inherent advantages over other methods for detecting pathogenic bacteria and viruses. Horseradish peroxidase mediated catalysis of substrate coloration is the key for the detection of target substances in ELISA. However, the variability, high cost, and environmental susceptibility of natural peroxidase greatly limit the application of ELISA in pathogen detection. Compared with natural enzymes, nanomaterials with enzyme-mimicking activity are inexpensive, highly environmentally stable, easy to store and mass producing, etc. Based on their peroxidase-like activities and unique physicochemical properties, nanomaterials can greatly improve the efficiency and ease of use of ELISA-like detection methods for pathogenic bacteria and viruses. This review introduces recent advances in the application of nanomaterials with peroxidase-like activity for the detection of pathogenic bacteria (both gram-negative bacteria and gram-positive bacteria) and viruses (both RNA viruses and DNA viruses). The emphasis is on the detection principle and the evaluation of effectiveness. The limitations and prospects for future translations are also discussed.

Azide-Masked Fluorescence Turn-On Probe for Imaging Mycobacteria

A fluorescence turn-on probe, an azide-masked and trehalose-derivatized carbazole (Tre-Cz), was developed to image mycobacteria. The fluorescence turn-on is achieved by photoactivation of the azide, which generates a fluorescent product through an efficient intramolecular C-H insertion reaction. The probe is highly specific for mycobacteria and could image mycobacteria in the presence of other Gram-positive and Gram-negative bacteria. Both the photoactivation and detection can be accomplished using a handheld UV lamp, giving a limit of detection of 10³ CFU/mL, which can be visualized by the naked eye. The probe was also able to image mycobacteria spiked in sputum samples, although the detection sensitivity was lower. Studies using heat-killed, stationary-phase, and isoniazid-treated mycobacteria showed that metabolically active bacteria are required for the uptake of Tre-Cz. The uptake decreased in the presence of trehalose in a concentration-dependent manner, indicating that Tre-Cz hijacked the trehalose uptake pathway. Mechanistic studies demonstrated that the trehalose transporter LpqY-SugABC was the primary pathway for the uptake of Tre-Cz. The uptake decreased in the LpqY-SugABC deletion mutants ΔlpqY, ΔsugA, ΔsugB, and ΔsugC and fully recovered in the complemented strain of ΔsugC. For the mycolyl transferase antigen 85 complex (Ag85), however, only a slight reduction of uptake was observed in the Ag85 deletion mutant ΔAg85C, and no incorporation of Tre-Cz into the outer membrane was observed. The unique intracellular incorporation mechanism of Tre-Cz through the LpqY-SugABC transporter, which differs from other trehalose-based fluorescence probes, unlocks potential opportunities to bring molecular cargoes to mycobacteria for both fundamental studies and theranostic applications.

Evaluation of an AI-Based TB AFB Smear Screening System for Laboratory Diagnosis on Routine Practice

The most robust and economical method for laboratory diagnosis of tuberculosis (TB) is to identify mycobacteria acid-fast bacilli (AFB) under acid-fast staining, despite its disadvantages of low sensitivity and labor intensity. In recent years, artificial intelligence (AI) has been used in TB-smear microscopy to assist medical technologists with routine AFB smear microscopy. In this study, we evaluated the performance of a TB automated system consisting of a microscopic scanner and recognition program powered by artificial intelligence and machine learning. This AI-based system can detect AFB and classify the level from 0 to 4+. A total of 5930 smears were evaluated on the performance of this automatic system in identifying AFB in daily lab practice. At the first stage, 120 images were analyzed per smear, and the accuracy, sensitivity, and specificity were 91.3%, 60.0%, and 95.7%, respectively. In the second stage, 200 images were analyzed per smear, and the accuracy, sensitivity, and specificity were increased to 93.7%, 77.4%, and 96.6%. After removing disqualifying smears caused by poor staining quality and smear preparation, the accuracy, sensitivity, and specificity were improved to 95.2%, 85.7%, and 96.9%, respectively. Furthermore, the automated system recovered 85 positive smears initially identified as negative by manual screening. Our results suggested that the automated TB system could achieve higher sensitivity and laboratory efficiency than manual microscopy under the quality control of smear preparation. Automated TB smear screening systems can serve as a screening tool at the first screen before manual microcopy.

Evaluation of MetaSystems Automated Fluorescent Microscopy System for the Machine-Assisted Detection of Acid-Fast Bacilli in Clinical Samples

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 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 ≥2+ 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.

Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis - A multi-center double blind study

Due to COVID-19 pandemic, there is a large global drop in the number of newly diagnosed cases with tuberculosis (TB) worldwide. Actions to mitigate and reverse the impact of the COVID-19 pandemic on TB are urgently needed. Recent development of TB smear microscopy automation systems using artificial intelligence may increase the sensitivity of TB smear microscopy. The objective is to evaluate the performance of an automation system (μ-Scan 2.0, Wellgen Medical) over manual smear microscopy in a multi-center, double-blind trial. Total of 1726 smears were enrolled. Referee medical technician and culture served as primary and secondary gold standards for result discrepancy. Results showed that, compared to manual microscopy, the μ-Scan 2.0's performance of accuracy, sensitivity and specificity were 95.7% (1651/1726), 87.7% (57/65), and 96.0% (1594/1661), respectively. The negative predictive value was 97.8% at prevalence of 8.2%. Manual smear microscopy remains the primary diagnosis of pulmonary tuberculosis (TB). Use of automation system could achieve higher TB smear sensitivity and laboratory efficiency. It can also serve as a screening tool that complements molecular methods to reduce the total cost for TB diagnosis and control. Furthermore, such automation system is capable of remote access by internet connection and can be deployed in area with limited medical resources.

Improved Conventional and New Approaches in the Diagnosis of Tuberculosis

Tuberculosis (TB) is a life-threatening infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis). Timely diagnosis and effective treatment are essential in the control of TB. Conventional smear microscopy still has low sensitivity and is unable to reveal the drug resistance of this bacterium. The traditional culture-based diagnosis is time-consuming, since usually the results are available after 3–4 weeks. Molecular biology methods fail to differentiate live from dead M. tuberculosis, while diagnostic immunology methods fail to distinguish active from latent TB. In view of these limitations of the existing detection techniques, in addition to the continuous emergence of multidrug-resistant and extensively drug-resistant TB, in recent years there has been an increase in the demand for simple, rapid, accurate and economical point-of-care approaches. This review describes the development, evaluation, and implementation of conventional diagnostic methods for TB and the rapid new approaches for the detection of M. tuberculosis.

Recent technical advances in whole slide imaging instrumentation

Microscopic observation of biological specimen smears is the mainstay of diagnostic pathology, as defined by the Digital Pathology Association. Though automated systems for this are commercially available, their bulky size and high cost renders them unusable for remote areas. The research community is investing much effort towards building equivalent but portable, low-cost systems. An overview of such research is presented here, including a comparative analysis of recent reports. This paper also reviews recently reported systems for automated staining and smear formation, including microfluidic devices; and optical and computational automated microscopy systems including smartphone-based devices. Image pre-processing and analysis methods for automated diagnosis are also briefly discussed. It concludes with a set of foreseeable research directions that could lead to affordable, integrated and accurate whole slide imaging systems.

Neoteric advancements in TB diagnostics and its future frame

Tuberculosis (TB) is one of the major infectious disease that causes threat to human health and leads to death in most of the cases. Mycobacterium tuberculosis is the causative agent that can affect both pulmonary and extra pulmonary regions of the body. This infection can be presented either as an active or latent form in the patients. Although this disease has been declared curable and preventable by WHO, it still holds its position as a global emergency. Over the past decade many hurdles such as low immunity, co-infections like HIV, autoimmune disorders, poverty, malnutrition and emerging trends in drug resistance patterns are hindering the eradication of this infection. However, many programmes have been launched by WHO with involvement of governments at various level to put a full stop over the disease. Under the Revised National Tuberculosis Control Programme (RNTCP) which was recently renamed as National Tuberculosis Elimination Programme (NTEP), the major focus is on eliminating tuberculosis by the year 2025. The main aim of the programme is to identify feasible quality testing, evaluate through NIKSHYA poshak yozana, restrict through BCG vaccination and assemble with public awareness to eradicate MTB. Numerous novel diagnostic techniques and molecular tools have been developed to elucidate and differentiate report of various suspected and active tuberculosis patients. However, improvements are still required to cut short the duration of the overall process ranging from screening of patients to their successful treatment.

Artificial Intelligence-Based Screening for Mycobacteria in Whole-Slide Images of Tissue Samples

OBJECTIVES

This study aimed to develop and validate a deep learning algorithm to screen digitized acid fast-stained (AFS) slides for mycobacteria within tissue sections.

METHODS

A total of 441 whole-slide images (WSIs) of AFS tissue material were used to develop a deep learning algorithm. Regions of interest with possible acid-fast bacilli (AFBs) were displayed in a web-based gallery format alongside corresponding WSIs for pathologist review. Artificial intelligence (AI)-assisted analysis of another 138 AFS slides was compared to manual light microscopy and WSI evaluation without AI support.

RESULTS

Algorithm performance showed an area under the curve of 0.960 at the image patch level. More AI-assisted reviews identified AFBs than manual microscopy or WSI examination (P < .001). Sensitivity, negative predictive value, and accuracy were highest for AI-assisted reviews. AI-assisted reviews also had the highest rate of matching the original sign-out diagnosis, were less time-consuming, and were much easier for pathologists to perform (P < .001).

CONCLUSIONS

This study reports the successful development and clinical validation of an AI-based digital pathology system to screen for AFBs in anatomic pathology material. AI assistance proved to be more sensitive and accurate, took pathologists less time to screen cases, and was easier to use than either manual microscopy or viewing WSIs.

Clinical Performance of Mycobacterial Immunohistochemistry in Anatomic Pathology Specimens

OBJECTIVES

Diagnosis of mycobacterial infections poses significant challenges in anatomic pathology. We recently described the use of antimycobacteria immunohistochemistry (IHC) as a sensitive, efficient diagnostic tool and now report the clinical performance of this assay among general, noninfectious disease pathology-trained anatomic pathologists.

METHODS

Over a 2-year period, all cases were retrospectively identified in which mycobacterial IHC was performed during routine diagnostic workup.

RESULTS

From October 2017 to September 2019, mycobacterial IHC was evaluated for 267 cases, resulting in 58 (22%) positive stains. Compared with culture and molecular results, the sensitivity and specificity of IHC were 52% and 80%, respectively. IHC performed significantly better than acid-fast bacilli (AFB) staining (Ziehl-Neelsen) (P < .0001; sensitivity 21%, specificity 92%) but similarly to modified AFB staining (mAFB; Fite-Faraco) (P = .9; sensitivity 61%, specificity 84%). In cases with discordant IHC and mAFB staining, there were no differences in rates of culture or polymerase chain reaction-confirmed positivity.

CONCLUSIONS

Mycobacterial IHC was well adopted with superior clinical performance to AFB and comparable performance to mAFB. These results support the use of IHC as an adjunctive tool in the diagnosis of mycobacterial infections and suggests its potential role as a rapid screening test for molecular testing.

Computer Vision and Artificial Intelligence Are Emerging Diagnostic Tools for the Clinical Microbiologist

Artificial intelligence (AI) is increasingly becoming an important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these solutions have the potential to improve a test's turnaround time, quality, and cost. A study by Mathison et al. used computer vision AI (B. A. Mathison, J. L. Kohan, J. F. Walker, R. B. Smith, et al., J Clin Microbiol 58:e02053-19, 2020, https://doi.org/10.1128/JCM.02053-19), but additional opportunities for AI applications exist within the clinical microbiology laboratory. Large data sets within clinical microbiology that are amenable to the development of AI diagnostics include genomic information from isolated bacteria, metagenomic microbial findings from primary specimens, mass spectra captured from cultured bacterial isolates, and large digital images, which is the medium that Mathison et al. chose to use. AI in general and computer vision in specific are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology.