68 Ga-nitroimidazole PET/CT imaging of hypoxia in tuberculosis: A case series.

Tuberculosis (TB) lesions in humans have been proven to be severely hypoxic with hypoxia leading to latency and dormancy of disease. Dormant TB lesions become less susceptible to standard TB treatment regimens with varying responses to treatment but may have increased susceptibility to nitroimidazole drugs. This in turn implies that positron emission tomography / computed tomography (PET/CT) imaging with radiolabelled nitroimidazoles may identify patients who will benefit from treatment with antimicrobial agents that are active against anaerobic bacteria. This case series aims to highlight the hypoxic uptake and retention of a novel 68 Ga-labelled hypoxia-seeking agent in TB lesions at different time points during anti-TB therapy using PET/CT imaging. Patients with confirmed TB underwent whole-body PET/CT after administration of a 68 Ga-nitroimidazole derivative at baseline and follow-up. Images were analysed both qualitatively and semi-quantitatively. Hypoxic uptake and change in uptake over time were analysed using lesion-to-muscle ratio (LMR) and lesion-to-blood ratio (LBR). 68 Ga-nitroimidazole avid lesions were demonstrated most frequently in the upper lobes of the lung. Low-grade hypoxic uptake was visualised in areas of consolidation, cavitation, nodules and lymph nodes. From baseline to follow-up imaging, the LMR increased with persistent hypoxic load despite morphologic improvement. This case series highlights the dynamic hypoxic microenvironment in TB lesions. From these initial data, it appears that 68 Ga-nitroimidazole is a promising candidate for monitoring hypoxic load in patients diagnosed with TB. Such imaging could identify patients who would benefit from individualised therapy targeting other mechanisms in the TB microenvironment with the intention to predict or improve treatment response.

An efficient deep learning-based framework for tuberculosis detection using chest X-ray images.

Early diagnosis of tuberculosis (TB) is an essential and challenging task to prevent disease, decrease mortality risk, and stop transmission to other people. The chest X-ray (CXR) is the top choice for lung disease screening in clinics because it is cost-effective and easily accessible in most countries. However, manual screening of CXR images is a heavy burden for radiologists, resulting in a high rate of inter-observer variances. Hence, proposing a cost-effective and accurate computer aided diagnosis (CAD) system for TB diagnosis is challenging for researchers. In this research, we proposed an efficient and straightforward deep learning network called TBXNet, which can accurately classify a large number of TB CXR images. The network is based on five dual convolutions blocks with varying filter sizes of 32, 64, 128, 256 and 512, respectively. The dual convolution blocks are fused with a pre-trained layer in the fusion layer of the network. In addition, the pre-trained layer is utilized for transferring pre-trained knowledge into the fusion layer. The proposed TBXNet has achieved an accuracy of 98.98%, and 99.17% on Dataset A and Dataset B, respectively. Furthermore, the generalizability of the proposed work is validated against Dataset C, which is based on normal, tuberculous, pneumonia, and COVID-19 CXR images. The TBXNet has obtained the highest results in Precision (95.67%), Recall (95.10%), F1-score (95.38%), and Accuracy (95.10%), which is comparatively better than all other state-of-the-art methods.

A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning.

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.

Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19.

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to detect every possible condition by building multiple separate systems, each of which detects one or more pre-specified conditions. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For training and tuning the system, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system trained using a large dataset containing a diverse array of CXR abnormalities generalizes to new patient populations and unseen diseases. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist. Lastly, to facilitate the continued development of AI models for CXR, we release our collected labels for the publicly available dataset.

Tuberculosis and COVID-19: An overlapping situation during pandemic.

INTRODUCTION: The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease 2019 (COVID-19). First COVID-19 case was detected in March, 10, 2020 in Turkey and as of May, 18, 2020 148,067 cases have been identified and 4096 citizens have died. Tuberculosis (TB) is a worldwide public health concern, incidence of tuberculosis (per 100,000 people) in Turkey was reported at 14, 1 in 2018. During pandemic COVID-19 was the main concern in every clinic and as we discuss here overlapping respiratory diseases may result in delaying of the diagnosis and treatment. METHODOLOGY: There were 4605 respiratory samples examined between March 23 and May 18 for COVID-19 and 185 samples for Mycobacterium tuberculosis in our laboratory. The Xpert Ultra assay was performed for the diagnosis of pulmonary tuberculosis; SARS-CoV-2 RNA was determined by real-time PCR (RT-PCR) analysis in combined nasopharyngeal and deep oropharyngeal swabs of suspected cases of COVID-19. RESULTS: Both of SARS-CoV-2 and M. tuberculosis tests were requested on the clinical and radiological grounds in 30 patients. Here we discussed 2 patients who were both COVID-19 and TB positive. One patient already diagnosed with tuberculosis become COVID-19 positive during hospitalization and another patient suspected and treated for COVID-19 received the final diagnosis of pulmonary TB and Human Immunodeficiency Virus infection. CONCLUSIONS: We want to emphasize that while considering COVID-19 primarily during these pandemic days, we should not forget one of the "great imitators", tuberculosis within differential diagnoses.

Convalescent (immune) plasma treatment in a myelodysplastic COVID-19 patient with disseminated tuberculosis.

During the ongoing COVID-19 pandemic due to the SARS-CoV-2 virus of which evidence-based medical paradigms cannot be easily applied; difficult clinical decisions shall be required particularly in the 'difficult-to-treat' cases of high risk group with associated comorbidities. Convalescent immune plasma therapy is a promising option as a sort of 'rescue' treatment in COVID-19 immune syndrome, where miraculous antiviral drugs are not available yet. In this report, we aim to convey our experience of multi-task treatment approach with convalescent immune plasma and anti-cytokine drug combination in a COVID-19 patient with extremely challenging comorbidities including active myeloid malignancy, disseminated tuberculosis and kidney failure.