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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., (© 2024. The Author(s).)

SHINE (Shorter Treatment for Minimal Tuberculosis in Children) was the first Phase 3 paediatric TB treatment-shortening trial. Robust chest X-ray (CXR) classification methods were integral to excluding severe disease for trial eligibility and to retrospectively adjudicating TB status at baseline. We describe and critically evaluate the CXR classification approaches and processes used in the SHINE trial.Children with non-severe TB were randomised to 4- vs 6-months anti-TB treatment. Radiologically non-severe TB was defined on CXR. CXRs were systematically interpreted by on-site clinicians prospectively for eligibility determination and retrospectively by experts to inform adjudication of baseline TB status and disease severity.A screening CXR was successfully obtained from all 1,204 enrolled children; 1,134 CXRs from children with intra-thoracic TB were reviewed by expert readers. Compared with the expert panel, enrolling clinicians classified more CXRs as abnormal and 'typical TB' and all as radiologically non-severe. The expert panel retrospectively classified 71/1,134 (6%) CXRs as severe. Of these, 4 (5.6%) had unfavourable outcomes compared with 34 (3.0%) in the trial overall.Using CXRs to classify radiological disease severity and inform eligibility decisions in real-time by local enrolling clinicians was feasible and safe in this large paediatric TB trial. Retrospective central expert CXR review was successful. Refinement of the CXR methods for the classification of both disease severity and TB status could support standardised implementation in routine care and research..

Objective: In Viet Nam, tuberculosis (TB) prevalence surveys revealed that approximately 98% of individuals with pulmonary TB have TB-presumptive abnormalities on chest radiographs, while 32% have no TB symptoms. This prompted the adoption of the "Double X" strategy, which combines chest radiographs and computer-aided detection with GeneXpert testing to screen for and diagnose TB among vulnerable populations. The aim of this study was to describe demographic, clinical and radiographic characteristics of symptomatic and asymptomatic Double X participants and to assess multilabel radiographic abnormalities on chest radiographs, interpreted by computer-aided detection software, as a possible tool for detecting TB-presumptive abnormalities, particularly for subclinical TB., Methods: Double X participants with TB-presumptive chest radiographs and/or TB symptoms and known risks were referred for confirmatory GeneXpert testing. The demographic and clinical characteristics of all Double X participants and the subset with confirmed TB were summarized. Univariate and multivariable logistic regression modelling was used to evaluate associations between participant characteristics and subclinical TB and between computer-aided detection multilabel radiographic abnormalities and TB., Results: From 2020 to 2022, 96 631 participants received chest radiographs, with 67 881 (70.2%) reporting no TB symptoms. Among 1144 individuals with Xpert-confirmed TB, 51.0% were subclinical. Subclinical TB prevalence was higher in older age groups, non-smokers, those previously treated for TB and the northern region. Among 11 computer-aided detection multilabel radiographic abnormalities, fibrosis was associated with higher odds of subclinical TB., Discussion: In Viet Nam, Double X community case finding detected pulmonary TB, including subclinical TB. Computer-aided detection software may have the potential to identify subclinical TB on chest radiographs by classifying multilabel radiographic abnormalities, but further research is needed., Competing Interests: ALI, GLH, TBPN, VHV, THTL, TTTL, VL, VCT, NDBT and THM received salary support from the United States Agency for International Development (USAID) Contract No. AID-440-C-16-00001 and Agreement No. 72044020CA00002. AM and ND were funded by the same USAID Contract and Agreement to conduct statistical analyses for this work. HMP (USAID) participated in the conceptualization of the work and in the manuscript review; USAID as the funding organization was otherwise not involved in the design, conceptualization, analysis or manuscript preparation. VLD, BHN, TTHT, VCN and VNN have no conflicts of interest to declare., ((c) 2024 The authors; licensee World Health Organization.)

Chest X-ray is an important diagnostic aid frequently used alongside microscopic smear of sputum for the confirmation of pulmonary tuberculosis (TB). However, there is a dearth of literature investigating the clinical and radiological pattern of sputum positive pulmonary TB among adults in Bangladesh. The current study explored these patterns in presentation. This descriptive cross-sectional study was conducted at outpatients in department of medicine of a tertiary care hospital. A total of 50 newly diagnosed adult cases of smear positive pulmonary TB attending at the Directly Observed Treatment Short-course (DOTS) corners were consecutively included. Informed written consent was taken before inclusion. Data were collected through face-to-face interview. Radiological presentation was explored using chest X-ray. Data were analyzed by SPSS version 26.0. The average age of patients was 41.0±17.12 years and majority were male (78.0%). The most prevalent respiratory symptom was cough (80.0%) followed by constitutional symptom like fever (70.0%) and weight loss (72.0%). Wasting was the predominant sign (60.0%). Radiologically both lungs were involved in 32.0%, left lung were involved in 30.0% cases and right lung were involved in 26.0% of cases. Twelve percent (12.0%) of patients had normal chest X-ray. Upper zone involvement was commonly observed in this study's patients (66.0%). The predominant pattern was consolidation (46.0%) followed by fibrosis (26.0%), nodular opacity (12.0%), collapse (10.0%), cavity (6.0%), pleural effusion (2.0%) and bronchiectasis (2.0%). Findings of this study would help familiarize and identify the common clinical and radiological presentations of sputum positive pulmonary TB patients in day-to-day practice.

Objectives: Computer-aided detection (CAD) software packages quantify tuberculosis (TB)-compatible chest X-ray (CXR) abnormality as continuous scores. In practice, a threshold value is selected for binary CXR classification. We assessed the diagnostic accuracy of an alternative approach to applying CAD for TB triage: incorporating CAD scores in multivariable modeling., Methods: We pooled individual patient data from four studies. Separately, for two commercial CAD, we used logistic regression to model microbiologically confirmed TB. Models included CAD score, study site, age, sex, human immunodeficiency virus status, and prior TB. We compared specificity at target sensitivities ≥90% between the multivariable model and the current threshold-based approach for CAD use., Results: We included 4,733/5,640 (84%) participants with complete covariate data (median age 36 years; 45% female; 22% with prior TB; 22% people living with human immunodeficiency virus). A total of 805 (17%) had TB. Multivariable models demonstrated excellent performance (areas under the receiver operating characteristic curve [95% confidence interval]: software A, 0.91 [0.90-0.93]; software B, 0.92 [0.91-0.93]). Compared with threshold scores, multivariable models increased specificity (e.g., at 90% sensitivity, threshold vs model specificity [95% confidence interval]: software A, 71% [68-74%] vs 75% [74-77%]; software B, 69% [63-75%] vs 75% [74-77%])., Conclusion: Using CAD scores in multivariable models outperformed the current practice of CAD-threshold-based CXR classification for TB diagnosis., Competing Interests: Declarations of competing interest FAK currently holds a grant from CIHR to study CAD in Canada. FAK also reports salary support from the Fonds de Recherche du Quebec Santé. FAK reports that the following developers of computer-aided detection software provided his research group with either free or reduced pricing access to their software for evaluative research, governed by contracts with the Research Institute of the McGill University Health Centre that ensure that the groups did not have any role in the study design, analysis, result interpretation, or decision to publish previous research and the submitted work: Delft (Netherlands, makers of CAD4TB), qure.ai (India, makers of qXR), and Lunit (South Korea, makers of LUNIT INSIGHT). MB reports grants from European and Development Countries Clinical Trials Partnership during the conduct of the study., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

According to the 2022 World Health Organization's Global Tuberculosis (TB) report, an estimated 10.6 million people fell ill with TB, and 1.6 million died from the disease in 2021. In addition, 2021 saw a reversal of a decades-long trend of declining TB infections and deaths, with an estimated increase of 4.5% in the number of people who fell ill with TB compared to 2020, and an estimated yearly increase of 450,000 cases of drug resistant TB. Estimating the severity of pulmonary TB using frontal chest X-rays (CXR) can enable better resource allocation in resource constrained settings and monitoring of treatment response, enabling prompt treatment modifications if disease severity does not decrease over time. The Timika score is a clinically used TB severity score based on a CXR reading. This work proposes and evaluates three deep learning-based approaches for predicting the Timika score with varying levels of explainability. The first approach uses two deep learning-based models, one to explicitly detect lesion regions using YOLOV5n and another to predict the presence of cavitation using DenseNet121, which are then utilized in score calculation. The second approach uses a DenseNet121-based regression model to directly predict the affected lung percentage and another to predict cavitation presence using a DenseNet121-based classification model. Finally, the third approach directly predicts the Timika score using a DenseNet121-based regression model. The best performance is achieved by the second approach with a mean absolute error of 13-14% and a Pearson correlation of 0.7-0.84 using three held-out datasets for evaluating generalization., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Purpose: To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from community-acquired pneumonia (CAP)., Methods: A total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis., Results: Two clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value., Conclusions: This study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Wang, Tang, Li, Han, Zhou, Wu and Wang.)

Tuberculosis (TB) is the leading cause of mortality among infectious diseases globally. Effectively managing TB requires early identification of individuals with TB disease. Resource-constrained settings often lack skilled professionals for interpreting chest X-rays (CXRs) used in TB diagnosis. To address this challenge, we developed "DecXpert" a novel Computer-Aided Detection (CAD) software solution based on deep neural networks for early TB diagnosis from CXRs, aiming to detect subtle abnormalities that may be overlooked by human interpretation alone. This study was conducted on the largest cohort size to date, where the performance of a CAD software (DecXpert version 1.4) was validated against the gold standard molecular diagnostic technique, GeneXpert MTB/RIF, analyzing data from 4363 individuals across 12 primary health care centers and one tertiary hospital in North India. DecXpert demonstrated 88% sensitivity (95% CI 0.85-0.93) and 85% specificity (95% CI 0.82-0.91) for active TB detection. Incorporating demographics, DecXpert achieved an area under the curve of 0.91 (95% CI 0.88-0.94), indicating robust diagnostic performance. Our findings establish DecXpert's potential as an accurate, efficient AI solution for early identification of active TB cases. Deployed as a screening tool in resource-limited settings, DecXpert could enable early identification of individuals with TB disease and facilitate effective TB management where skilled radiological interpretation is limited., (© 2024. The Author(s).)

Introduction: Chest X-ray (CXR) remains one of the tools used in diagnosing tuberculosis (TB). However, few studies about such tools exist, specifically in children in Indonesia. We aim to investigate and compare the CXR findings of children with pulmonary drug-resistant TB (DR-TB) and drug-sensitive TB (DS-TB) that could help in the evaluation and management of TB cases in children., Methods: Retrospective analysis with cross-sectional approach was conducted in children (<18 years old) diagnosed with pulmonary DR-TB and DS-TB from January 2018 to December 2021. Documented data were collected from the Paediatric Respirology Registry and Tuberculosis Information System at Dr. Hasan Sadikin General Hospital Bandung. Characteristics of children, CXR findings, and TB severity were assessed and compared using the chi-square and Fisher's exact tests with significance levels set at p value <0.05., Results: Sixty-nine children (DR-TB 31 children vs. DS-TB 38 children) were assessed. Of the 31 children with DR-TB, 65% were classified as multidrug-resistant TB (MDR-TB), followed by rifampicin-resistant TB (RR-TB), pre-extensively drug-resistant TB (pre-XDR-TB), and extensively drug-resistant TB (XDR-TB). The most common CXR findings in DR-TB are consolidation (68%), fibrosis (42%), and cavity (29%), whereas in DS-TB, it is pleura effusion (37%). Severe TB accounts for 50% of DR-TB (p = 0.008)., Conclusions: Consolidation, fibrosis, cavities, and findings of severe TB are most common in DR-TB. Pleural effusion is the most common in DS-TB. These findings have the potential to be considered in further examination of children with pulmonary DR-TB and DS-TB; hence, more extensive studies are needed to confirm these results., (© 2024 The Author(s). The Clinical Respiratory Journal published by John Wiley & Sons Ltd.)

Background: Computer-aided detection (CAD) can help identify people with active tuberculosis left undetected. However, few studies have compared the performance of commercially available CAD products for screening in high tuberculosis and high HIV settings, and there is poor understanding of threshold selection across products in different populations. We aimed to compare CAD products' performance, with further analyses on subgroup performance and threshold selection., Methods: We evaluated 12 CAD products on a case-control sample of participants from a South African tuberculosis prevalence survey. Only those with microbiological test results were eligible. The primary outcome was comparing products' accuracy using the area under the receiver operating characteristic curve (AUC) against microbiological evidence. Threshold analyses were performed based on pre-defined criteria and across all thresholds. We conducted subgroup analyses including age, gender, HIV status, previous tuberculosis history, symptoms presence, and current smoking status., Findings: Of the 774 people included, 516 were bacteriologically negative and 258 were bacteriologically positive. Diverse accuracy was noted: Lunit and Nexus had AUCs near 0·9, followed by qXR, JF CXR-2, InferRead, Xvision, and ChestEye (AUCs 0·8-0·9). XrayAME, RADIFY, and TiSepX-TB had AUC under 0·8. Thresholds varied notably across these products and different versions of the same products. Certain products (Lunit, Nexus, JF CXR-2, and qXR) maintained high sensitivity (>90%) across a wide threshold range while reducing the number of individuals requiring confirmatory diagnostic testing. All products generally performed worst in older individuals, people with previous tuberculosis, and people with HIV. Variations in thresholds, sensitivity, and specificity existed across groups and settings., Interpretation: Several previously unevaluated products performed similarly to those evaluated by WHO. Thresholds differed across products and demographic subgroups. The rapid emergence of products and versions necessitates a global strategy to validate new versions and software to support CAD product and threshold selections., Funding: Government of Canada., Competing Interests: Declaration of interests CMD declares research grants from US National Institutes of Health, German Ministry of Education and Research, German Alliance for Global Health Research, United States Agency for International Development, FIND, German Center for Infection Research, and WHO. CMD also declares that she serves as an academic editor for PLoS Medicine and on the WHO Technical Advisory Group on tuberculosis diagnostics. All other authors declare no competing interests., (Copyright © 2024 World Health Organization. Published by Elsevier Ltd. This is an Open Access article published under the CC BY 3.0 IGO license which permits users to download and share the article for non-commercial purposes, so long as the article is reproduced in the whole without changes, and provided the original source is properly cited. This article shall not be used or reproduced in association with the promotion of commercial products, services or any entity. There should be no suggestion that WHO endorses any specific organisation, products or services. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.)

Objective: To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB)., Methods: In this retrospective analysis, medical records of 99 individuals afflicted with NTM-PD and 285 individuals with PTB in Zhejiang Chinese and Western Medicine Integrated Hospital were examined. Random numbers generated by a computer were utilized to stratify the study cohort, with 80% designated as the training cohort and 20% as the validation cohort. A total of 2153 radiomics features were extracted using Python (Pyradiomics package) to analyse the CT characteristics of the large disease areas. The identification of significant factors was conducted through the least absolute shrinkage and selection operator (LASSO) regression. The following four supervised learning classifier models were developed: random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost). For assessment and comparison of the predictive performance among these models, receiver-operating characteristic (ROC) curves and the areas under the ROC curves (AUCs) were employed., Results: The Student's t -test, Levene test, and LASSO algorithm collectively selected 23 optimal features. ROC analysis was then conducted, with the respective AUC values of the XGBoost, LR, SVM, and RF models recorded to be 1, 0.9044, 0.8868, and 0.7982 in the training cohort. In the validation cohort, the respective AUC values of the XGBoost, LR, SVM, and RF models were 0.8358, 0.8085, 0.87739, and 0.7759. The DeLong test results noted the lack of remarkable variation across the models., Conclusion: The CT radiomics features can help distinguish between NTM-PD and PTB. Among the four classifiers, SVM showed a stable performance in effectively identifying these two diseases.

Objective: The purpose of this study was to explore the auxiliary diagnostic value of volumetric CT value in quantifying the activity of a pulmonary tuberculoma., Methods: Chest CT image data of 112 patients with pulmonary tuberculomas who were diagnosed clinically between October 16, 2013 and March 21, 2023 were selected. With the shortest diameter axis>5 mm on the mediastinal window serving as the inclusion criterion, 108 active tuberculomas and 64 non-active tuberculomas were selected. The focused image was manually segmented using ITK-SNAP software, the volumetric CT value of the focus was calculated, and the ROC curve was analyzed. Using the final clinical diagnosis as the reference standard, the auxiliary diagnostic efficacy and consistency of the conventional CT film reading method and volumetric CT value in determining the activity of a pulmonary tuberculoma were compared., Results: The volumetric CT value of 108 active pulmonary tuberculoma lesions (33.39 [28.17,36.23] HU) was significantly less than 64 inactive pulmonary tuberculoma lesions (78.91 [57.81,120.31] HU); the difference was statistically significant (Z = -10.888. P < 0.001). ROC curve analysis showed that at a maximum Yoden index value of 0.963, the optimal volumetric CT threshold value was 45.32 HU, the sensitivity and specificity of the volumetric CT value in determining the activity of a pulmonary tuberculoma were 97.2% and 100.0%, respectively, and the maximum area under the ROC curve was 0.998. Taking the final clinical diagnosis as the reference standard, the sensitivity, specificity, consistency, and kappa value of the conventional CT film reading method for determining the activity of a pulmonary tuberculoma were 72.2% (78/108), 70.3% (45/64), 71.5% (123/172), and 0.413, respectively, while the corresponding volumetric CT values were 97.2% (105/108), 100.0% (64/64), 98.3% (168/172), and 0.951, respectively., Conclusion: Accurately quantifying the volumetric CT value of a pulmonary tuberculoma focus determines the activity of a pulmonary tuberculoma, which has very important auxiliary diagnostic value., Competing Interests: No authors have competing interests., (Copyright: © 2024 Wei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: To evaluate diagnostic yield and feasibility of integrating testing for TB and COVID-19 using molecular and radiological screening tools during community-based active case-finding (ACF)., Methods: Community-based participants with presumed TB and/or COVID-19 were recruited using a mobile clinic. Participants underwent simultaneous point-of-care (POC) testing for TB (sputum; Xpert Ultra) and COVID-19 (nasopharyngeal swabs; Xpert SARS-CoV-2). Sputum culture and SARS-CoV-2 RT-PCR served as reference standards. Participants underwent ultra-portable POC chest radiography with computer-aided detection (CAD). TB infectiousness was evaluated using smear microscopy, cough aerosol sampling studies (CASS), and chest radiographic cavity detection. Feasibility of POC testing was evaluated via user-appraisals., Results: Six hundred and one participants were enrolled, with 144/601 (24.0%) reporting symptoms suggestive of TB and/or COVID-19. 16/144 (11.1%) participants tested positive for TB, while 10/144 (6.9%) tested positive for COVID-19 (2/144 [1.4%] had concurrent TB/COVID-19). Seven (7/16 [43.8%]) individuals with TB were probably infectious. Test-specific sensitivity and specificity (95% CI) were: Xpert Ultra 75.0% (42.8-94.5) and 96.9% (92.4-99.2); Xpert SARS-CoV-2 66.7% (22.3-95.7) and 97.1% (92.7-99.2). Area under the curve (AUC) for CAD4TB was 0.90 (0.82-0.97). User appraisals indicated POC Xpert to have 'good' user-friendliness., Conclusions: Integrating TB/COVID-19 screening during community-based ACF using POC molecular and radiological tools is feasible, has a high diagnostic yield, and can identity probably infectious persons., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it., Methods: Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks., Results: Our ML-based CT analysis (TB-Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI's CLIP Vi4). TB-Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB-lesion volumes, and disease-signs during disease pathogenesis., Conclusion: The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Growing evidence suggests that chronic inflammation caused by tuberculosis (TB) may increase the incidence of diabetes. However, the relationship between post-TB pulmonary abnormalities and diabetes has not been well characterized., Methods: We analyzed data from a cross-sectional study in KwaZulu-Natal, South Africa, of people 15 years and older who underwent chest X-ray and diabetes screening with hemoglobin A1c testing. The analytic sample was restricted to persons with prior TB, defined by either (1) a self-reported history of TB treatment, (2) radiologist-confirmed prior TB on chest radiography, and (3) a negative sputum culture and GeneXpert. Chest X-rays of all participants were evaluated by the study radiologist to determine the presence of TB lung abnormalities. To assess the relationships between our outcome of interest, prevalent diabetes (HBA1c ≥6.5%), and our exposure of interest, chest X-ray abnormalities, we fitted logistic regression models adjusted for potential clinical and demographic confounders. In secondary analyses, we used the computer-aided detection system CAD4TB, which scores X-rays from 10 to 100 for detection of TB disease, as our exposure interest, and repeated analyses with a comparator group that had no history of TB disease., Results: In the analytic cohort of people with prior TB (n = 3,276), approximately two-thirds (64.9%) were women, and the average age was 50.8 years (SD 17.4). The prevalence of diabetes was 10.9%, and 53.0% of people were living with HIV. In univariate analyses, there was no association between diabetes prevalence and radiologist chest X-ray abnormalities (OR 1.23, 95%CI 0.95-1.58). In multivariate analyses, the presence of pulmonary abnormalities was associated with an 29% reduction in the odds of prevalent diabetes (aOR 0.71, 95%CI 0.53-0.97, p = 0.030). A similar inverse relationship was observed for diabetes with each 10-unit increase in the CAD4TB chest X-ray scores among people with prior TB (aOR 0.92, 95%CI 0.87-0.97; p = 0.002), but this relationship was less pronounced in the no TB comparator group (aOR 0.96, 95%CI 0.94-0.99)., Conclusions: Among people with prior TB, pulmonary abnormalities on digital chest X-ray are inversely associated with prevalent diabetes. The severity of radiographic post-TB lung disease does not appear to be a determinant of diabetes in this South African population., (© 2024. The Author(s).)

Objective: The objective of this study was to explore the added value of spectral computed tomography (CT) parameters to conventional CT features for differentiating tuberculosis-associated fibrosing mediastinitis (TB-associated FM) from endobronchial lung cancer (EBLC)., Methods: Chest spectral CT enhancement images from 109 patients with atelectasis were analyzed retrospectively. These patients were divided into two distinct categories: the TB-associated FM group (n = 77) and the EBLC group (n = 32), based on bronchoscopy and/or pathological findings. The selection of spectrum parameters was optimized with the least absolute shrinkage and selection operator regression analysis. The relationship between the spectrum parameters and conventional parameters was explored using Pearson's correlation. Multivariate logistic regression analysis was used to build spectrum model. The spectrum parameters in the spectrum model were replaced with their corresponding conventional parameters to build the conventional model. Diagnostic performances were evaluated using receiver operating characteristic curve analyses., Results: There was a moderate correlation between the parameters ㏒(L-AEF NIC ) - ㏒(L-AEF C ) (r= 0.419; p< 0.0001), ㏒(O-AEF 40KeV ) - ㏒(O-AEF C ) (r= 0.475; p< 0.0001), [L-A-hydroxyapatite {HAP}(I)] - (L-U-CT) (r= 0.604; p< 0.0001), {arterial enhancement fraction (AEF) derived from normalized iodine concentration (NIC) of lymph node (L-AEF NIC ), AEF derived from CT 40KeV of bronchial obstruction (O-AEF 40KeV ), arterial-phase Hydroxyapatite (Iodine) concentration of lymph node [L-A-HAP(I)], AEF derived from conventional CT (AEF C ), unenhanced CT value (U-CT)}. Spectrum model could improve diagnostic performances compared to conventional model (area under curve: 0.965 vs 0.916, p= 0.038)., Conclusion: There was a moderate correlation between spectrum parameters and conventional parameters. Integrating conventional CT features with spectrum parameters could further improve the ability in differentiating TB-associated FM from EBLC., (Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Respiratory diseases have a significant global impact, and assessing these conditions is crucial for improving patient outcomes. Chest X-ray is widely used for diagnosis, but expert evaluation can be challenging. Automatic computer-aided diagnosis methods can provide support for clinicians in these tasks. Deep learning has emerged as a set of algorithms with exceptional potential in such tasks. However, these algorithms require a vast amount of data, often scarce in medical imaging domains. In this work, a new data augmentation methodology based on adapted generative latent diffusion models is proposed to improve the performance of an automatic pathological screening in two high-impact scenarios: tuberculosis and lung nodules. The methodology is evaluated using three publicly available datasets, representative of real-world settings. An ablation study obtained the highest-performing image generation model configuration regarding the number of training steps. The results demonstrate that the novel set of generated images can improve the performance of the screening of these two highly relevant pathologies, obtaining an accuracy of 97.09%, 92.14% in each dataset of tuberculosis screening, respectively, and 82.19% in lung nodules. The proposal notably improves on previous image generation methods for data augmentation, highlighting the importance of the contribution in these critical public health challenges., (© 2024. The Author(s).)

In tuberculosis (TB), chest radiography (CXR) patterns are highly variable, mimicking pneumonia and many other diseases. This study aims to evaluate the efficacy of Google teachable machine, a deep neural network-based image classification tool, to develop algorithm for predicting TB probability of CXRs. The training dataset included 348 TB CXRs and 3806 normal CXRs for training TB detection. We also collected 1150 abnormal CXRs and 627 normal CXRs for training abnormality detection. For external validation, we collected 250 CXRs from our hospital. We also compared the accuracy of the algorithm to five pulmonologists and radiological reports. In external validation, the AI algorithm showed areas under the curve (AUC) of 0.951 and 0.975 in validation dataset 1 and 2. The accuracy of the pulmonologists on validation dataset 2 showed AUC range of 0.936-0.995. When abnormal CXRs other than TB were added, AUC decreased in both human readers (0.843-0.888) and AI algorithm (0.828). When combine human readers with AI algorithm, the AUC further increased to 0.862-0.885. The TB CXR AI algorithm developed by using Google teachable machine in this study is effective, with the accuracy close to experienced clinical physicians, and may be helpful for detecting tuberculosis by CXR., (© 2024. The Author(s).)

BACKGROUND Lung cancer is the most common malignant neoplasm diagnosed worldwide. Early diagnosis and treatment are of great importance for patient's prognosis. A wide variety of pulmonary conditions display clinical and radiological presentation similar to that of lung cancer, and the awareness of their existence can help in making correct diagnoses. CASE REPORT This article presents a description of 4 patients with an insidious type of lesions mimicking pulmonary carcinomas. The first patient was referred to Department with a tumor-like lesion in the right lung. After CT of the chest and core-needle biopsy, the lesion turned out to be an ectopic thyroid tissue. The second patient reported a dry cough and weight loss. A lung nodule mass was revealed in chest CT and the patient was diagnosed with pulmonary tuberculoma. The remaining 2 patients, despite the suspicion of lung cancer, were subsequently diagnosed with a post-traumatic pleural hematoma and diffuse large B cell lymphoma. CONCLUSIONS Low-dose computed tomography of the chest plays a significant role in the diagnosis of newly detected lesions in the lungs. However, due to the similarity of the image of cancer to that of other diseases, the ultimate diagnosis should be based on the interpretation of full imaging diagnostic tests, clinical presentation, and histopathological examination of the material obtained from the lesion. Analysis of cases enables us to expand our understanding of the diseases that need to be considered in differential diagnosis of a patient with a detected tumor-like lesion in the lungs.

Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments. This study aimed to identify factors influencing treatment success and culture conversion, focusing on artificial intelligence (AI)-based chest X-ray analysis and Xpert MTB/RIF assay cycle threshold (Ct) values. In this retrospective study across six South Korean referral centers (January 1 to December 31, 2019), we included adults with rifampicin-susceptible pulmonary tuberculosis confirmed by Xpert assay from sputum samples. We analyzed patient characteristics, AI-based tuberculosis extent scores from chest X-rays, and Xpert Ct values. Of 230 patients, 206 (89.6%) achieved treatment success. The median age was 61 years, predominantly male (76.1%). AI-based radiographic tuberculosis extent scores (median 7.5) significantly correlated with treatment success (odds ratio [OR] 0.938, 95% confidence interval [CI] 0.895-0.983) and culture conversion at 8 weeks (liquid medium: OR 0.911, 95% CI 0.853-0.973; solid medium: OR 0.910, 95% CI 0.850-0.973). Sputum smear positivity was 49.6%, with a median Ct of 26.2. However, Ct values did not significantly correlate with major treatment outcomes. AI-based radiographic scoring at diagnosis is a significant predictor of treatment success and culture conversion in pulmonary tuberculosis, underscoring its potential in personalized patient management., (© 2024. The Author(s).)

Background: An accurate and non-invasive approach is urgently needed to distinguish tuberculosis granulomas from lung adenocarcinomas. This study aimed to develop and validate a nomogram based on contrast enhanced-compute tomography (CE-CT) to preoperatively differentiate tuberculosis granuloma from lung adenocarcinoma appearing as solitary pulmonary solid nodules (SPSN)., Methods: This retrospective study analyzed 143 patients with lung adenocarcinoma (mean age: 62.4 ± 6.5 years; 54.5% female) and 137 patients with tuberculosis granulomas (mean age: 54.7 ± 8.2 years; 29.2% female) from two centers between March 2015 and June 2020. The training and internal validation cohorts included 161 and 69 patients (7:3 ratio) from center No.1, respectively. The external testing cohort included 50 patients from center No.2. Clinical factors and conventional radiological characteristics were analyzed to build independent predictors. Radiomics features were extracted from each CT-volume of interest (VOI). Feature selection was performed using univariate and multivariate logistic regression analysis, as well as the least absolute shrinkage and selection operator (LASSO) method. A clinical model was constructed with clinical factors and radiological findings. Individualized radiomics nomograms incorporating clinical data and radiomics signature were established to validate the clinical usefulness. The diagnostic performance was assessed using the receiver operating characteristic (ROC) curve analysis with the area under the receiver operating characteristic curve (AUC)., Results: One clinical factor (CA125), one radiological characteristic (enhanced-CT value) and nine radiomics features were found to be independent predictors, which were used to establish the radiomics nomogram. The nomogram demonstrated better diagnostic efficacy than any single model, with respective AUC, accuracy, sensitivity, and specificity of 0.903, 0.857, 0.901, and 0.807 in the training cohort; 0.933, 0.884, 0.893, and 0.892 in the internal validation cohort; 0.914, 0.800, 0.937, and 0.735 in the external test cohort. The calibration curve showed a good agreement between prediction probability and actual clinical findings., Conclusion: The nomogram incorporating clinical factors, radiological characteristics and radiomics signature provides additional value in distinguishing tuberculosis granuloma from lung adenocarcinoma in patients with a SPSN, potentially serving as a robust diagnostic strategy in clinical practice., (© 2024. The Author(s).)

The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.

Background: Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseases. Settings with a high burden of tuberculosis (TB) and people living with HIV can potentially benefit from the use of AI to augment resource-constrained healthcare systems., Objective: To assess the utility of qXR software (AI) in detecting radiological changes compatible with lung cancer or pulmonary TB (PTB)., Methods: We performed an observational study in a tertiary institution that serves a population with a high burden of lung cancer and PTB. In total, 382 chest radiographs that had a confirmed diagnosis were assessed: 127 with lung cancer, 144 with PTB and 111 normal. These chest radiographs were de-identified and randomly uploaded by a blinded investigator into qXR software. The output was generated as probability scores from predefined threshold values., Results: The overall sensitivity of the qXR in detecting lung cancer was 84% (95% confidence interval (CI) 80 - 87%), specificity 91% (95% CI 84 - 96%) and positive predictive value of 97% (95% CI 95 - 99%). For PTB, it had a sensitivity of 90% (95% CI 87 - 93%) and specificity of 79% (95% CI 73 - 84%) and negative predictive value of 85% (95% CI 79 - 91%)., Conclusion: The qXR software was sensitive and specific in categorising chest radiographs as consistent with lung cancer or TB, and can potentially aid in the earlier detection and management of these diseases.

This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3D CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model's output, which can identify lesions in important parts of the lungs and make transparent decisions., (© 2024. International Federation for Medical and Biological Engineering.)

TB is a public health problem, and its diagnosis can be challenging. Among imaging methods, chest X-ray (CXR) is the leading choice for assessing pulmonary TB (PTB). Recent advancements in the field of artificial intelligence have stimulated studies evaluating the performance of machine learning (ML) for medical diagnosis. This study validated a new original Brazilian tool, the XmarTB, applied to CXR images to support the early diagnosis of PTB.An ML model was trained on 3,800 normal images, 3,800 abnormal CXRs without PTB and 1,376 with PTB manifestations from the publicly available TBX11K database.The binary classification can distinguish between normal and abnormal CXR with a sensitivity of 99.4% and specificity of 99.4%. The XmarTB tool had a sensitivity of 98.1% and a specificity of 99.7% in detecting TB cases among CXRs with abnormal CXRs; sensitivity was 96.7% and specificity 98.7% in detecting TB cases among all samples.This diagnostic tool can accurately and automatically detect abnormal CXRs and satisfactorily differentiate PTB from other pulmonary diseases. This tool holds significant promise in aiding the proactive detection of TB cases, providing rapid and accurate support for early diagnosis..

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Cicero Ferreira Fernandes Costa Filho reports financial support was provided by Samsung Electronics of Amazonia Ltda. Marly Guimaraes Fernandes Costa reports financial support was provided by Samsung Electronics of Amazonia Ltda. Mikaela Kalline Serrao reports financial support was provided by Samsung Electronics of Amazonia Ltda. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Timor-Leste has one of the world's highest estimated tuberculosis (TB) incidences, yet the data which informs this estimate is limited and the true burden of TB disease is not known. TB prevalence surveys offer the best means of determining robust estimates of disease burden. This study aims to provide an estimate of the prevalence of bacteriologically confirmed pulmonary TB in Timor-Leste and provide additional insights into diagnostic coverage and health-seeking behaviour of persons with symptoms suggestive of TB., Methods and Analysis: A national population-based cross-sectional cluster survey will be conducted in which participants aged 15 years and older will be screened for pulmonary TB using an algorithm consisting of symptom screening and digital X-ray of the chest with computer-aided detection software for X-ray interpretation. Xpert Ultra and liquid culture methods will be used to confirm survey TB cases. Additional data will be collected from persons reporting symptoms suggestive of TB to assess health-seeking behaviour and access to TB diagnosis and care. The survey aims to screen a target sample population of 20 068 people, living within 50 clusters, representing every municipality of Timor-Leste. Bacteriologically confirmed pulmonary TB prevalence will be estimated using WHO-recommended methods., Ethics and Dissemination: Research ethics approval has been granted by the human research ethics committee of the Northern Territory, Australia, and the Instituto Nacional da Saúde, Timor-Leste. The results will be published in a peer-reviewed scientific journal and disseminated with relevant stakeholders., Trial Registration Number: ACTRN12623000718640., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Prevalence surveys in Ethiopia indicate smear negative pulmonary tuberculosis (SNPTB) taking the major share of the overall TB burden. It has also been a diagnostic dilemma worldwide leading to diagnostic delays and difficulty in monitoring treatment outcomes. This study determines and compares the clinical and imaging findings in SNPTB and smear positive PTB (SPPTB). Methodology . A case-control study was conducted on 313 PTB (173 SNPTB) patients. Data and sputum samples were collected from consented patients. Smear microscopy, GeneXpert, and culture analyses were performed on sputum samples. Data were analyzed using Stata version 17; a P value < 0.05 was considered statistically significant., Results: Of the 173 SNPTB patients, 42% were culture positive with discordances between test results reported by health facilities and Armauer Hansen Research Institute laboratory using concentrated smear microscopy. A previous history of TB and fewer cavitary lesions were significantly associated with SNPTB., Conclusions: Though overall clinical presentations of SNPTB patients resemble those seen in SPPTB patients, a prior history of TB was strongly associated with SNPTB. Subject to further investigations, the relatively higher discrepancies seen in TB diagnoses reflect the posed diagnostic challenges in SNPTB patients, as a higher proportion of these patients are also seen in Ethiopia., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2024 Alem Alemayehu et al.)

Patient delay increases the morbidity and mortality due to tuberculosis (TB). This study aimed to assess patient delay among patients with pulmonary tuberculosis in Yantai from 2013 to 2022, and to analyze factors related to patient delay. Data of patients with pulmonary tuberculosis in Yantai City from 2013 to 2022 were obtained from the Tuberculosis Management Information System of the Chinese Disease Prevention and Control System. Statistical analyses were performed using the SPSS.26.0 software. The trend in patient delay rate was tested using the chi-square trend test. Univariate analyses were performed using the chi-square test, and factors with statistically significant differences in the univariate analysis were included in the binary logistic regression analysis to identify the factors affecting patient delay. Patient delay was defined as an interval of more than 14 days between the onset of clinical symptoms and the patient first visit to a healthcare facility. From 2013 to 2022, the median delay time for patients with pulmonary tuberculosis in Yantai was 28 ± 52 days and the patient delay rate was 69.5%. There was an overall increasing trend in the rate of patient delay as the number of years increased. Univariate analyses revealed statistically significant differences in patient delay in terms of age, occupation, patient source, domicile, pathogenetic results, and the presence of comorbidities (all P < .05). The results of logistic regression analysis showed that the age was 20 to 39, 40 to 59, and ≥ 60 years (OR = 1.365, 95%CI: 1.156-1.612; OR = 1.978, 95%CI: 1.660-2.356; OR = 1.767, 95%CI: 1.480-2.110), occupation was domestic and un-employed (OR = 1.188, 95%CI: 1.071-1.317), domicile as mobile population (OR = 1.212, 95%CI: 1.099-1.337), and positive pathogenic results (OR = 1.242, 95%CI: 1.015-1.520) were risk factors for patient delay. Patient delays were serious among pulmonary tuberculosis patients in Yantai City, 2013 to 2022, and patient delay was related to factors such as age, occupation, domicile, patient source, and pathogenetic results., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Pulmonary Tuberculosis (PTB) is an infectious disease caused by a bacterium called Mycobacterium tuberculosis. This paper aims to create Symbolic Artificial Intelligence (SAI) system to diagnose PTB using clinical and paraclinical data. Usually, the automatic PTB diagnosis is based on either microbiological tests or lung X-rays. It is challenging to identify PTB accurately due to similarities with other diseases in the lungs. X-ray alone is not sufficient to diagnose PTB. Therefore, it is crucial to implement a system that can diagnose based on all paraclinical data. Thus, we propose in this paper a new PTB ontology that stores all paraclinical tests and clinical symptoms. Our SAI system includes domain ontology and a knowledge base with performance indicators and proposes a solution to diagnose current and future PTB also abnormal patients. Our approach is based on a real database of more than four years from our collaborators at Pondicherry hospital in India.

Purpose: In this study, we aimed to report the feasibility and quality of fast (unenhanced < 10-min duration) magnetic resonance imaging (MRI) for the detection of lymphadenopathy in non-sedated children with suspected tuberculosis (TB)., Material and Methods: This was a prospective study that involved children (< 13 years of age) hospitalised at Red Cross Children's Hospital with suspected pulmonary TB who were referred for a fast MRI of the chest. The limited short-duration MRI protocol included coronal short tau inversion recovery (STIR) and axial diffusion-weighted imaging (DWI) sequences with additional axial STIR and axial and coronal T2 sequences if the patient was compliant. The scan time was capped at 10 min and a study was considered successfully completed when DWI and STIR images were obtained in axial planes. MRI quality was recorded as 'acceptable quality'; 'poor quality, but readable'; and 'non-diagnostic'., Results: Of the 192 fast MRI protocol scans, 166 (86%) were successfully completed within the 10-min allotted scan period. There was no age or sex difference between successful and unsuccessful studies. The mean duration of successful scans was 6.5 min (standard deviation = 1.5 min, range = 4-10 min)., Conclusion: Fast (sub-10-min scan) MRI is feasible for diagnosis of lymphadenopathy in non-sedated children in the setting of suspected TB, including those below 6 years of age., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Rationale: Imaging studies are widely performed when treating Mycobacterium avium complex pulmonary disease (MAC-PD); however, the clinical significance of post-treatment radiographic change is unknown. Objectives: To determine whether a deep neural network trained with pulmonary tuberculosis could adequately score the radiographic severity of MAC-PD and then to examine relationships between post-treatment radiographic severity and its change from baseline and long-term prognosis. Methods: We retrospectively collected chest radiographs of adult patients with MAC-PD treated for ⩾6 months at baseline and at 3, 6, 9, and 12 months of treatment. We correlated the radiographic severity score generated by a deep neural network with visual and clinical severity as determined by radiologists and mycobacterial culture status, respectively. The associations between the score, improvement from baseline, and mortality were analyzed using Cox proportional hazards regression. Results: In total, 342 and 120 patients were included in the derivation and validation cohorts, respectively. The network's severity score correlated with radiologists' grading (Spearman coefficient, 0.40) and mycobacterial culture results (odds ratio, 1.02; 95% confidence interval [CI], 1.0-1.05). A significant decreasing trend in the severity score was observed over time ( P  < 0.001). A higher score at 12 months of treatment was independently associated with higher mortality (adjusted hazard ratio, 1.07; 95% CI, 1.03-1.10). Improvements in radiographic scores from baseline were associated with reduced mortality, regardless of culture conversion (adjusted hazard ratio, 0.42; 95% CI, 0.22-0.80). These findings were replicated in the validation cohort. Conclusions: Post-treatment radiographic severity and improvement from baseline in patients with MAC-PD were associated with long-term survival.

Aim: To compare chest radiography (CXR) findings in human immunodeficiency virus (HIV)-positive and HIV-negative children who had microbiologically confirmed pulmonary tuberculosis (PTB)., Materials and Methods: Retrospective analysis of CXRs from children with known HIV status and microbiologically confirmed PTB (culture or GeneXpert Xpert MTB/RIF positive), who were hospitalised or seen at a primary healthcare centre over a 5-year period. Radiological findings were compared according to HIV and nutritional status., Results: CXRs of 130 children were analysed from 35 (27%) HIV- positive and 95 (73%) HIV-negative children with confirmed PTB, median age 45.7 months (interquartile range [IQR] 18-81.3 months). CXR changes consistent with PTB were reported in 21/35 (60%) of HIV-positive and 59/95 (62%) of HIV-negative patients, (p=0.81). Normal CXR was identified in 3/35 (8.6%) of HIV-positive and 5/95 (5.3%) of HIV-negative patients (p=0.81). Airway compression was present in 3/35 (8.6%) of HIV-positive and 7/95 (7.4%) of HIV-negative patients (p>0.99). Overall, lymphadenopathy was identified in 42/130 (32.3%) of patients, 11/35 (31.4 %) were HIV-positive compared with 31/95 (32.6%) HIV-negative patients. Airspace consolidation was present in 60% of both HIV-positive (21/35) and HIV-negative patients (57/95). Pleural effusion was present in 2/35 (5.7 %) of HIV-negative and 9/95 (9.5 %) of HIV-negative patients. There were no statistically significant radiological differences by HIV group., Conclusion: There were no significant differences in the CXR findings between the HIV-positive and HIV-negative children with confirmed PTB., (Copyright © 2023. Published by Elsevier Ltd.)

Background: The differentiation between latent tuberculosis infection (LTBI) and tuberculosis (TB) relies on radiological changes. Confirming the diagnosis remains a challenge because typical findings are often missing in children. This study evaluates diagnostic sensitivity, specifity and interobserver agreement on the radiological diagnosis of TB by chest-x-rays in accordance to professional specialization and work experience., Methods: Chest x-rays of 120 children with proven tuberculosis infection were independently evaluated by general radiologists, paediatric radiologists and paediatric pulmonologists. Results were compared to a reference diagnosis created by group of experienced paediatric radiologists and paediatric pulmonologists. Primary endpoints were diagnostic sensitivity and specificity and interobserver variability defined as Krippendorfs alpha of thesel groups compared to the reference diagnosis., Results: Of the 120 chest x-rays 33 (27,5%) were diagnosed as TB by the reference standard . Paediatric pulmonologist had the highest diagnostic sensitivity (90%) but were less specific (71%) whereas general radiologist were less sensitive (68%) but more secific (95%). The best diagnostic accuracy was achieved by pediatric radiologists with a diagnostic sensitivity of 77% and specificity 95% respectively., Conclusions: We demonstrated significant interobserver variability and relevant differences in sensitivity and specificity in the radiological diagnosis of TB between the groups. Paediatric radiologists showed the best diagnostic performance. As the diagnosis of pulmonary TB has significant therapeutic consequences for children they should be routinely involved in the diagnostic process., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background: Incipient tuberculosis, a progressive state of Mycobacterium tuberculosis infection with an increased risk of developing into tuberculosis disease, remains poorly characterised. Animal models suggest an association of progressive infection with bacteraemia. Circulating M tuberculosis DNA has previously been detected in pulmonary tuberculosis by use of Actiphage, a bacteriophage-based real-time PCR assay. We aimed to investigate whether serial [ 18 F]fluorodeoxyglucose ([ 18 F]FDG)-PET-CT could be used to characterise the state and progressive trajectory of incipient tuberculosis, and examine whether these PET-CT findings are associated with Actiphage-based detection of circulating M tuberculosis DNA., Methods: We did a prospective 12-month cohort study in healthy, asymptomatic adults (aged ≥16 years) who were household contacts of patients with pulmonary tuberculosis, and who had a clinical phenotype of latent tuberculosis infection, in Leicester, UK. Actiphage testing of participants' blood samples was done at baseline, and [ 18 F]FDG PET-CT at baseline and after 3 months. Baseline PET-CT features were classified as positive, indeterminate, or negative, on the basis of the quantitation (maximum standardised uptake value [SUV max ]) and distribution of [ 18 F]FDG uptake. Microbiological sampling was done at amenable sites of [ 18 F]FDG uptake. Changes in [ 18 F]FDG uptake after 3 months were quantitatively categorised as progressive, stable, or resolving. Participants received treatment if features of incipient tuberculosis, defined as microbiological detection of M tuberculosis or progressive PET-CT change, were identified., Findings: 20 contacts were recruited between Aug 5 and Nov 5, 2020; 16 of these participants had a positive result on IFNγ release assay (QuantiFERON-TB Gold Plus [QFT]) indicating tuberculosis infection. Baseline PET-CT scans were positive in ten contacts (all QFT positive), indeterminate in six contacts (three QFT positive), and negative in four contacts (three QFT positive). Four of eight PET-CT-positive contacts sampled had M tuberculosis identified (three through culture, one through Xpert MTB/RIF Ultra test) from intrathoracic lymph nodes or bronchial wash and received full antituberculosis treatment. Two further unsampled PET-CT-positive contacts were also treated: one with [ 18 F]FDG uptake in the lung (SUV max 9·4) received empirical antituberculosis treatment and one who showed progressive [ 18 F]FDG uptake received preventive treatment. The ten untreated contacts with [ 18 F]FDG uptake at baseline (seven QFT positive) had stable or resolving changes at follow-up and remained free of tuberculosis disease after 12 months. A positive baseline Actiphage test was associated with the presence of features of incipient tuberculosis requiring treatment (p=0·018)., Interpretation: Microbiological and inflammatory features of incipient tuberculosis can be visualised on PET-CT and are associated with M tuberculosis detection in the blood, supporting the development of pathogen-directed blood biomarkers of tuberculosis risk., Funding: MRC Confidence in Concept., Competing Interests: Declaration of interests PH has received consultancy fees for two advisory board meetings with PBD Biotech since study inception. CR and BS hold stocks in PBD Biotech, the company commercialising the Actiphage technology. CR and BS have a pending patent on Actiphage bacteriophage-mediated mycobacterial DNA release assay testing (PCT/GB2020/050524). PBD Biotech supplied PCR reagents and components of the Actiphage kit for use in the study but had no role in design, execution, analysis, or reporting. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Stage 1 of the STREAM trial demonstrated that the 9 month (Short) regimen developed in Bangladesh was non-inferior to the 20 month (Long) 2011 World Health Organization recommended regimen. We assess the association between HIV infection and radiographic manifestations of tuberculosis and factors associated with time to culture conversion in Stage 1 of the STREAM trial., Methods: Reading of chest radiographs was undertaken independently by two clinicians, and films with discordant reading were read by a third reader. Recording of abnormal opacity of the lung parenchyma included location (right upper, right lower, left upper, and left lower) and extent of disease (minimal, moderately-advanced, and far advanced). Time to culture conversion was defined as the number of days from initiation of treatment to the first of two consecutive negative culture results, and compared using the log-rank test, stratified by country. Cox proportional hazards models, stratified by country and adjusted for HIV status, were used to identify factors associated with culture conversion., Results: Of the 364 participants, all but one had an abnormal chest X-ray: 347 (95%) had opacities over upper lung fields, 318 (87%) had opacities over lower lung fields, 124 (34%) had far advanced pulmonary involvement, and 281 (77%) had cavitation. There was no significant association between HIV and locations of lung parenchymal opacities, extent of opacities, the presence of cavitation, and location of cavitation. Participants infected with HIV were significantly less likely to have the highest positivity grade (3+) of sputum culture (p = 0.035) as compared to participants not infected with HIV. Cavitation was significantly associated with high smear positivity grades (p < 0.001) and high culture positivity grades (p = 0.004) among all participants. Co-infection with HIV was associated with a shorter time to culture conversion (hazard ratio 1.59, 95% CI 1.05-2.40)., Conclusions: Radiographic manifestations of tuberculosis among the HIV-infected in the era of anti-retroviral therapy may not differ from that among those who were not infected with HIV. Radiographic manifestations were not consistently associated with time to culture conversion, perhaps indicating that the Short regimen is sufficiently powerful in achieving sputum conversion across the spectrum of radiographic pulmonary involvements., Trial Registration: ISRCTN ISRCTN78372190. Registered 14/10/2010. The date of first registration 10/02/2016., (© 2024. The Author(s).)

Background: Tuberculosis (TB) is one of the leading infectious causes of mortality globally. The purpose of this research is to examine the clinical and radiological characteristics of patients with TB and diabetes., Methods: The research comprised 276 TB patients, 52 of whom were diabetic and 224 of whom were not. During the evaluation of the patients' clinical histories, age, gender, diagnostic indicator, and whether or not they had undergone prior treatment were questioned, as were the requirement of inpatient treatment and the existence of drug resistance. Radiographically, they were questioned in terms of bilateral-unilateral extent, percentage of parenchymal involvement, cavitation, tree-in-bud appearance, the presence of ground glass, consolidation, miliary involvement, sequela fibrotic changes, parenchymal calcification, mediastinal lymphadenopathy, pleural effusion, and pleural calcification. In addition, segmenting was used to assess involvement in the affected lobes., Results: When we look at the results of 276 patients, 182 males and 94 females, the mean age is 46.01 ± 17.83. Diabetes and TB coexistence are more prevalent in male individuals (P = 0.029). Smear positivity and the need for inpatient treatment were found to be higher in the clinical features of diabetic patients (P = 0.05 and P = 0.01, respectively). Radiologically, diabetes individuals are more likely to have larger mediastinal lymph nodes (P = 0.032)., Conclusion: In the coexistence of both TB and diabetes, there are variations in radiological findings, complexity in treatment response, and patient management., (Copyright © 2024 Copyright: © 2024 International Journal of Mycobacteriology.)

Objective: This study aimed to explore the imaging characteristics of patients with pulmonary tuberculosis complicated with pulmonary embolism and analyze the prognosis of the condition, thereby reducing the mortality and misdiagnosis rate of complications in this type of pulmonary tuberculosis., Methods: In this retrospective study, a total of 70 patients diagnosed with pulmonary embolism by computed tomography pulmonary angiography (CTPA) from January 2016 to May 2021 in Anhui Chest Hospital were included. Among them, 35 patients with pulmonary embolism combined with pulmonary tuberculosis were set as the study group, and the other 35 patients with pulmonary embolism only were set as the control group. The imaging findings of chest CT examination, the incidence of pulmonary hypertension, the level of N-terminal proto-B-type brain natriuretic peptide (NT-proBNP), and the prognosis of patients were compared between the two groups. The incidence of deep venous embolism was evaluated by ultrasonography of the lower extremity., Results: In the study group, the median age of patients was 71 years, and the ratio of males to females was 2.5 to 1. In the control group, the median age was 66 years old, and the male-to-female ratio was 2.2 to 1. There were 16 cases (16/35, 45.71%) in the study group and 10 cases (10/35, 28.57%) in the control group with an increased level of NT-proBNP. Pulmonary hypertension occurred in 10 patients (10/35, 28.57%) in the study group and 7 patients (7/35, 20.00%) in the control group. Patients who lost follow-up included 5 in the study group (5/35, 14.29%) and 3 in the control group (3/35, 8.57%). There were 17 cases (17/35, 48.57%) in the study group and 3 cases (3/35, 8.57%) in the control group with pulmonary artery widening, and the difference was significant (P < 0.001). There were 13 deaths in the study group (13/35, 37.14%) and 1 death in the control group (1/35, 2.86%), and the difference was significant (P <0.001)., Conclusion: Special signs of pulmonary artery widening, pulmonary hypertension of varying degrees, and increased levels of NT-proBNP of varying degrees can be found in patients with pulmonary tuberculosis complicated with pulmonary embolism, and the three signs are positively correlated. The mortality of patients with pulmonary tuberculosis complicated with pulmonary embolism is significantly higher than that of patients with pulmonary embolism alone. Pulmonary tuberculosis and pulmonary embolism both occur in the ipsilateral lung, causing clinical symptoms to cover each other, thereby making diagnosis difficult., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Competing Interests: Disclosure: There is no conflict of interest or other disclosures.

Background: Chest radiographs (CXR) for tuberculosis (TB) screening in children are valuable in high-burden settings. However, less certain in low prevalence contexts. In the United States, positive PPD is sufficient to treat for "latent" TB, or TB infection in asymptomatic patients., Objective: We sought to determine frequency of abnormal CXR findings after a positive purified protein derivative (PPD) test at a tertiary pediatric center in the United States., Method: A retrospective evaluation was conducted of patients (0-18 years) with a CXR after a positive PPD (e.g., known exposure, employment, migratory requirements or before immunosuppression) between 2011 and 2021. Clinical information, demographics, and reason for PPD were recorded from health record. CXRs were evaluated using initial report and by a pediatric radiologist with special interest in TB and 8 years of experience., Result: Of 485 patients, median [interquartile range (IQR)] age 8.5[3.3-14.4], abnormal CXRs were described in 5 (1%). Most common reasons for PPD included: close contact with someone with TB or with high risk for TB. Most patients 373 (76.9%) received treatment for latent TB, and 111 (22.9%) no treatment. One patient (0.2%) received treatment for active disease. Radiographic findings included isolated lymphadenopathy (n = 2), consolidation (n = 1), pleural fluid/thickening (n = 1) and a patient with lymphadenopathy and a calcified nodule (n = 1)., Conclusion: In our experience, prevalence of chest radiographs findings for patients with positive PPD was very low. Moreover, no cases of severe disease were seen and those with abnormal findings would not merit treatment change under current WHO guidelines., Competing Interests: Declaration of competing interest The authors declare they have no conflict of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Introduction: Tuberculosis is an opportunist infection that is fatal and most frequently seen in HIV-positive patients due to immunosuppression. Endobronchial lesions can portray symptoms in different ways. Endobronchial Tuberculosis is one of these lesions., Case Report: An HIV-positive, untreated 26-year-old patient with fever, cough, and dyspnea consulted our clinic. In the chest X-ray taken, effusion on the right side and non-homogeneous density increase in the middle and upper lobes, bilaterally more prominent on the right side, were observed. Therefore, the patient underwent bronchoscopy because the CT (computerized tomography) showed mediastinal lymphadenopathy (LAP) and an endobronchial lesion in the left main bronchus. During bronchoscopy, a vegetative endobronchial lesion that causes obstruction in the left main bronchus was monitored. With the help of Pathology and PCR results, endobronchial tuberculosis was diagnosed., Conclusion: Even if Acid-alcohol-resistant Bacillus (ARB) is detected negative in patients who stop responding to antimicrobial treatment and are being monitored under radiological scanning, a distinctive diagnosis of endobronchial tuberculosis should be kept in mind while performing bronchoscopy., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Hospitalized patients with a high suspicion of pulmonary tuberculosis (HS-PTB) are isolated until a definite diagnosis can be determined. If doubt remains after negative sputum samples, bronchoscopy with bronchoalveolar lavage (BAL) is often sought. Still, evidence of the added value of BAL in this patient population is scarce. To address this issue, we included consecutive HS-PTB patients with negative sputum samples who underwent BAL between 2017 and 2018. Chest X-rays (CXR) and CT scans were evaluated by a chest radiologist blind to the final diagnosis. Independent predictors for PTB were assessed by multivariate regression, using all positive PTB patients between 2017 and 2019 (by sputum or BAL) as a control group ( n = 41). Overall, 42 HS-PTB patients were included (mean age 51 ± 9, 36% female). BAL was a viable diagnostic for PTB in three (7%) cases and for other clinically relevant pathogens in six (14%). Independent predictors for PTB were ≥2 sub-acute symptoms (adjusted OR 3.18, 95% CI 1.04-9.8), CXR upper-lobe consolidation (AOR 8.70, 95% CI 2.5-29), and centrilobular nodules in chest CT (AOR 3.96, 95% CI 1.20-13.0, p = 0.02). In conclusion, bronchoscopy with BAL in hospitalized patients with HS-PTB had a 7% added diagnostic value after negative sputum samples. Our findings highlight specific predictors for PTB diagnosis that could be used in future controlled studies to personalize the diagnostic evaluation.

Background: Early diagnosis and treatment of tuberculosis (TB) is one of the key strategies to achieve the WHO End TB targets. This study aimed to develop and validate a simple, convenient risk score to diagnose pulmonary TB among presumptive TB cases., Methods: This prediction model used Ethiopian national TB prevalence survey data and included 5459 presumptive TB cases from all regions of Ethiopia. Logistic regression was used to determine which variables are predictive of pulmonary TB. A risk prediction model was developed, incorporating significant variables (p<0.05). The Youden Index method was used to choose the optimal cut-off point to separate the risk score of the patients as high and low. Model performance was assessed using discrimination power and calibration. Internal validation of the model was assessed using Efron's enhanced bootstrap method, and the clinical utility of the risk score was assessed using decision curve analysis., Results: Of total participants, 94 (1.7%) were confirmed to have TB. The final prediction model included three factors with different scores: (1) TB contact history, (2) chest X-ray (CXR) abnormality and (3) two or more symptoms of TB. The optimal cut-off point for the risk score was 6 and was found to have a good discrimination accuracy (c-statistic=0.70, 95% CI: 0.65 to 0.75). The risk score has sensitivity of 51.1%, specificity of 79.9%, positive predictive value of 4.3% and negative predictive value of 98.9%. After internal validation, the optimism coefficient was 0.003, which indicates the model is internally valid., Conclusion: We developed a risk score that combines TB contact, number of TB symptoms and CXR abnormality to estimate individual risk of pulmonary TB among presumptive TB cases. Though the score is easy to calculate and internally validated, it needs external validation before widespread implementation in a new setting., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Subpleural micronodules and interlobular septal thickening are common CT scan findings in TB pleural effusion. These CT scan features could help us differentiate between TB pleural effusion and nonTB empyema., Research Question: Does the frequency of subpleural micronodules and interlobular septal thickening correlate with the presence of pleural effusion in patients with pulmonary TB?, Study Design and Methods: CT scan findings of pulmonary TB, micronodules and their distribution (peribronchovascular, septal, subpleural, centrilobular, and random), large opacity (consolidation/macronodule), cavitation, tree-in-buds, bronchovascular bundle thickening, interlobular septal thickening, lymphadenopathy, and pleural effusion were retrospectively analyzed. Patients were divided into two groups according to the presence of pleural effusion. Clinicoradiologic findings of the two groups were then analyzed. We presented Benjamini-Hochberg critical value for multiple testing correction of CT scan findings, with a false discovery rate of 0.05., Results: Of a total of 338 consecutive patients diagnosed with pulmonary TB who underwent CT scans, 60 were excluded because of coexisting pulmonary diseases. The frequency of subpleural nodules (47/68, 69% in pulmonary TB with pleural effusion vs 30/210, 14% in pulmonary TB without effusion, P < .001, Benjamini-Hochberg [B-H] critical value = 0.0036) and interlobular septal thickening (55/68, 81% vs 134/210, 64%, P = .009, B-H critical value = 0.0107) was significantly higher in the group of patients with pulmonary TB with pleural effusion than in the group without pleural effusion. In contrast, tree-in-buds (20/68, 29% vs 101/210, 48%, P = .007, B-H critical value = 0.0071) were less frequently seen in patients with pulmonary TB with pleural effusion., Interpretation: Subpleural nodules and septal thickening were more common in pulmonary TB patients with pleural effusion than in those without pleural effusion. TB involvement of the lymphatics in the peripheral interstitium could be associated with the development of pleural effusion., Competing Interests: Financial/Nonfinancial Disclosures None declared., (Copyright © 2023 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Introduction: This study aimed to develop and assess a deep-learning model based on CT images for distinguishing infectivity in patients with pulmonary tuberculosis (PTB)., Methods: We labeled all 925 patients from four centers with weak and strong infectivity based on multiple sputum smears within a month for our deep-learning model named TBINet's training. We compared TBINet's performance in identifying infectious patients to that of the conventional 3D ResNet model. For model explainability, we used gradient-weighted class activation mapping (Grad-CAM) technology to identify the site of lesion activation in the CT images., Results: The TBINet model demonstrated superior performance with an area under the curve (AUC) of 0.819 and 0.753 on the validation and external test sets, respectively, compared to existing deep learning methods. Furthermore, using Grad-CAM, we observed that CT images with higher levels of consolidation, voids, upper lobe involvement, and enlarged lymph nodes were more likely to come from patients with highly infectious forms of PTB., Conclusion: Our study proves the feasibility of using CT images to identify the infectivity of PTB patients based on the deep learning method., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gao, Zhang, Hu, He, Fu, Lin, Liu, Fu, Liu, Sun, Chen, Yang and Zhou.)