A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl–Neelsen Stain on Tissue

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

OBJECTIVE

To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.

DESIGN

We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.

RESULTS

Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.

CONCLUSION

The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

Artificial intelligence in the clinical laboratory

Laboratory medicine has become a highly automated medical discipline. Nowadays, artificial intelligence (AI) applied to laboratory medicine is also gaining more and more attention, which can optimize the entire laboratory workflow and even revolutionize laboratory medicine in the future. However, only a few commercially available AI models are currently approved for use in clinical laboratories and have drawbacks such as high cost, lack of accuracy, and the need for manual review of model results. Furthermore, there are a limited number of literature reviews that comprehensively address the research status, challenges, and future opportunities of AI applications in laboratory medicine. Our article begins with a brief introduction to AI and some of its subsets, then reviews some AI models that are currently being used in clinical laboratories or that have been described in emerging studies, and explains the existing challenges associated with their application and possible solutions, finally provides insights into the future opportunities of the field. We highlight the current status of implementation and potential applications of AI models in different stages of the clinical testing process.

Automatic bright-field smear microscopy for diagnosis of pulmonary tuberculosis

In recent decades, many studies have been published on the use of automatic smear microscopy for diagnosing pulmonary tuberculosis (TB). Most of them deal with a preliminary step of the diagnosis, the bacilli detection, whereas sputum smear microscopy for diagnosis of pulmonary TB comprises detecting and reporting the number of bacilli found in at least 100 microscopic fields, according to the 5 grading scales (negative, scanty, 1+, 2+ and 3+) endorsed by the World Health Organization (WHO). Pulmonary TB diagnosis in bright-field smear microscopy, however, depends upon the attention of a trained and motivated technician, while the automated TB diagnosis requires little or no interpretation by a technician. As far as we know, this work proposes the first automatic method for pulmonary TB diagnosis in bright-field smear microscopy, according to the WHO recommendations. The proposed method comprises a semantic segmentation step, using a deep neural network, followed by a filtering step aiming to reduce the number of false positives (false bacilli): color and shape filtering. In semantic segmentation, different configurations of encoders are evaluated, using depth-wise separable convolution layers and channel attention mechanism. The proposed method was evaluated with a large, robust, and annotated image dataset designed for this purpose, consisting of 250 testing sets, 50 sets for each of the 5 TB diagnostic classes. The following performance metrics were obtained for automatic pulmonary TB diagnosis by smear microscopy: mean precision of 0.894, mean recall of 0.896, and mean F1-score of 0.895.

First detection of bovine tuberculosis by Ziehl-Neelsen staining and polymerase chain reaction at dairy farms in the Lekok Sub-District, Pasuruan Regency, and Surabaya region, Indonesia

Background and Aim

Bovine tuberculosis (TB) is a zoonotic disease of great public health importance, particularly in Indonesia, where control measures are limited or are not implemented. This study aimed to detect the presence of Mycobacterium pathogens in milk samples from dairy cattle in Pasuruan regency and Surabaya City, East Java, using Ziehl-Neelsen acid-fast staining and polymerase chain reaction (PCR).

Materials and Methods

Milk samples were aseptically collected from 50 cattle in the Lekok Subdistrict, Pasuruan Regency, and 44 from dairy farms in the Lakarsantri Subdistrict, Wonocolo Subdistrict, Mulyorejo Subdistrict, and Kenjeran Subdistrict, Surabaya, East Java. To detect Mycobacteria at the species level, each sample was assessed by Ziehl-Neelsen staining and PCR using the RD1 and RD4 genes.

Results

The results of PCR assay from 50 samples in Lekok Subdistrict, Pasuruan Regency showed that 30 samples (60%) were positive for Mycobacterium tuberculosis and two samples (4%) were positive for Mycobacterium bovis, although Ziehl-Neelsen staining did not show the presence of Mycobacterium spp. In the Surabaya region, 31 samples (70.45%) were positive for M. tuberculosis and three samples (6.8%) were positive for M. bovis. Six samples (13.63%) from all PCR-positive samples could be detected microscopically with Ziehl-Neelsen.

Conclusion

The presence of bovine TB in this study supports the importance of using a molecular tool alongside routine surveillance for a better understanding of the epidemiology of bovine TB in East Java.

Evolution of Laboratory Diagnosis of Tuberculosis

Tuberculosis (TB) is an infectious disease of global public health importance caused by the Mycobacterium tuberculosis complex. Despite advances in diagnosis and treatment, this disease has worsened with the emergence of multidrug-resistant strains of tuberculosis. We aim to present and review the history, progress, and future directions in the diagnosis of tuberculosis by evaluating the current methods of laboratory diagnosis of tuberculosis, with a special emphasis on microscopic examination and cultivation on solid and liquid media, as well as an approach to molecular assays. The microscopic method, although widely used, has its limitations, and the use and evaluation of other techniques are essential for a complete and accurate diagnosis. Bacterial cultures, both in solid and liquid media, are essential methods in the diagnosis of TB. Culture on a solid medium provides specificity and accuracy, while culture on a liquid medium brings rapidity and increased sensitivity. Molecular tests such as LPA and Xpert MTB/RIF have been found to offer significant benefits in the rapid and accurate diagnosis of TB, including drug-resistant forms. These tests allow the identification of resistance mutations and provide essential information for choosing the right treatment. We conclude that combined diagnostic methods, using several techniques and approaches, provide the best result in the laboratory diagnosis of TB. Improving the quality and accessibility of tests, as well as the implementation of advanced technologies, is essential to help improve the sensitivity, efficiency, and accuracy of TB diagnosis.

Performance Evaluation of No-Code Artificial Intelligence Models for the Detection of Acid-Fast Bacilli: A Comparative Analysis of Three Models

Background Acid-fast bacilli Mycobacterium tuberculosis and Mycobacterium leprae are the causative organisms behind two major diseases of developing nations, tuberculosis and leprosy, respectively. To efficiently tackle these diseases in developing nations, drugs must be augmented with improved detection modalities. This necessitates the development of enhanced tools that can aid the current detection modalities being used in high-incidence areas. A no-code artificial intelligence model based on image classification is one such tool that can be used in the identification of acid-fast bacilli. This study utilizes three such no-code artificial intelligence models that originate from three different platforms but share identical training, testing, and subsequent evaluation. Thereafter, the study is directed at comparing the three models created and identifying the one that can function as a promising support system for the detection of acid-fast bacilli. Methods To begin with, a total of 1000 images per class, i.e., positive and negative for each disease, were captured from the diagnosed slides of tuberculosis and leprosy, taken from the Department of Pathology. Subsequently, these slides were reviewed again by a pathologist to demarcate them as positive or negative for acid-fast bacilli. Once the required number of images was captured, 600 images of each class were selected as the training set, 300 images as the testing set, and the remaining 100 images as the evaluation set. Data augmentation was then performed using techniques such as rotating, mirroring, cropping, and position shifting. These designated data sets were then used to train the image classification software available on the following three platforms: Lobe (Microsoft Corporation, Redmond, Washington, United States), Create ML (Apple Inc., Cupertino, California, United States), Python-based open-source software (PerceptiLabs, Stockholm, Sweden). The final evaluation was based on different parameters such as sensitivity, specificity, ease of use, learning curve, technological resources required, and feasibility of implementation. All parameters put together served the purpose of comparison to identify the most promising model. Results Out of the three models tested, the one built using Lobe is the most promising in terms of the evaluation parameters considered. For tuberculosis, the sensitivity and specificity values obtained were 96% each, while for leprosy, they were 100% and 96%, respectively. Also, the model built using Lobe had a near-negligible learning curve, in addition to being the most cost-effective and feasible model to implement. Furthermore, it had a unique real-time training feature, which constantly improved the model throughout the testing period, till the final sensitivity and specificity values were achieved. Conclusions In clinical situations where a high number of cases are encountered each day, a no-code artificial intelligence model built using Lobe would get exposed to a huge database, getting trained in real time. Subsequently, such a model would reach considerable levels of sensitivity and specificity and in turn, act as a promising support system for the detection of acid-fast bacilli.

Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

BACKGROUND

The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs).

METHODS

8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool's database. All teeth, crown-bridge restorations, dental implants, composite-amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique.

RESULTS

The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown-bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively.

CONCLUSIONS

Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.

A systematic review and repeatability study on the use of deep learning for classifying and detecting tuberculosis bacilli in microscopic images

Tuberculosis (TB) is among the leading causes of death worldwide from a single infectious agent. This disease usually affects the lungs (pulmonary TB) and can be cured in most cases with a quick diagnosis and proper treatment. Microscopic sputum smear is widely used to diagnose and manage pulmonary TB. Despite being relatively fast and low cost, it can be exhausting because it depends on manually counting TB bacilli (Mycobacterium tuberculosis) in microscope images. In this context, different Deep Learning (DL) techniques are proposed in the literature to assist in performing smear microscopy. This article presents a systematic review based on the PRISMA procedure, which investigates which DL techniques can contribute to classifying TB bacilli in microscopic images of sputum smears using the Ziehl-Nielsen method. After an extensive search and a careful inclusion/exclusion procedure, 28 papers were selected from a total of 400 papers retrieved from nine databases. Based on these articles, the DL techniques are presented as possible solutions to improve smear microscopy. The main concepts necessary to understand how such techniques are proposed and used are also presented. In addition, replication work is also carried out, verifying reproducibility and comparing different works in the literature. In this review, we look at how DL techniques can be a partner to make sputum smear microscopy faster and more efficient. We also identify some gaps in the literature that can guide which issues can be addressed in other works to contribute to the practical use of these methods in laboratories.

Editorial on Special Issue "Artificial Intelligence in Pathological Image Analysis"

The artificial intelligence (AI), especially deep learning models, is highly compatible with medical images and natural language processing and is expected to be applied to pathological image analysis and other medical fields [...].