AIMC Topic: Mycobacterium tuberculosis

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Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.

European journal of nuclear medicine and molecular imaging Jun 16, 2021
PURPOSE: To develop and evaluate the effectiveness of a deep learning framework (3D-ResNet) based on CT images to distinguish nontuberculous mycobacterium lung disease (NTM-LD) from Mycobacterium tuberculosis lung disease (MTB-LD).
Diagnosis, Differential Lung Diseases Tuberculosis Mycobacterium tuberculosis Nontuberculous Mycobacteria Humans Retrospective Studies Tomography, X-Ray Computed Deep Learning
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Automated detection of Mycobacterium tuberculosis using transfer learning.

Journal of infection in developing countries May 31, 2021
INTRODUCTION: Quantitative analysis of Mycobacterium tuberculosis using microscope is very critical for diagnosing tuberculosis diseases. Microbiologist encounter several challenges which can lead to misdiagnosis. However, there are 3 main challenges...
Sensitivity and Specificity Tuberculosis, Pulmonary Mycobacterium tuberculosis Diagnostic Tests, Routine Humans Machine Learning
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Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.

PloS one Mar 25, 2021
BACKGROUND AND OBJECTIVES: Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This st...
Humans Neural Networks, Computer Drug Resistance, Bacterial Male Middle Aged ROC Curve Logistic Models Area Under Curve Female Adult Sensitivity and Specificity Cross-Sectional Studies Antibiotics, Antitubercular Indonesia Tuberculosis Mycobacterium tuberculosis Rifampin
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A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers.

Tuberculosis (Edinburgh, Scotland) Jan 4, 2021
Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective ...
Host-Pathogen Interactions Lung Diagnosis, Computer-Assisted Radiography, Thoracic Deep Learning Predictive Value of Tests Mycobacterium tuberculosis Diffusion of Innovation Humans Automation Software Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Tuberculosis, Pulmonary
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Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning.

EBioMedicine Nov 7, 2020
BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversit...
Disease Susceptibility Tuberculosis Mycobacterium tuberculosis Molecular Imaging Immunohistochemistry Female Reproducibility of Results Animals Male Deep Learning Image Processing, Computer-Assisted Computational Biology Disease Models, Animal Machine Learning Algorithms Biomarkers Prognosis Humans
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Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.

Scientific reports Oct 22, 2020
Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce p...
Mutation, Missense Leprosy Machine Learning Mycobacterium leprae Drug Resistance, Bacterial Staphylococcus aureus Bacterial Proteins Staphylococcal Infections Humans Mycobacterium tuberculosis Antitubercular Agents Rifampin Tuberculosis
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Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease.

The Lancet. Digital health Oct 19, 2020
BACKGROUND: Deep learning-based radiological image analysis could facilitate use of chest x-rays as triage tests for pulmonary tuberculosis in resource-limited settings. We sought to determine whether commercially available chest x-ray analysis softw...
Male Middle Aged Lung Deep Learning Body Mass Index Age Factors Triage Mycobacterium tuberculosis HIV Infections Radiology Sex Factors Image Processing, Computer-Assisted Sputum X-Rays Female Humans Pakistan Young Adult Software Tuberculosis, Pulmonary Adult Prospective Studies Sensitivity and Specificity
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Machine-assisted interpretation of auramine stains substantially increases through-put and sensitivity of microscopic tuberculosis diagnosis.

Tuberculosis (Edinburgh, Scotland) Sep 19, 2020
Of all bacterial infectious diseases, infection by Mycobacterium tuberculosis poses one of the highest morbidity and mortality burdens on humans throughout the world. Due to its speed and cost-efficiency, manual microscopy of auramine-stained sputum ...
Benzophenoneidum Humans Sputum Microscopy, Fluorescence Tuberculosis Coloring Agents Retrospective Studies Mycobacterium tuberculosis Deep Learning Image Processing, Computer-Assisted Staining and Labeling
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Evaluation of magnesium oxide and zinc oxide nanoparticles against multi-drug-resistance Mycobacterium tuberculosis.

The Indian journal of tuberculosis Aug 4, 2020
OBJECTIVE: The current study has evaluated the MICs and MBCs of ZnONPs, MgONPs, and MgONPs-ZnONPs against HRv Mtb and MDR-Mtb.
Inhibitory Concentration 50 Magnesium Oxide Tuberculosis, Multidrug-Resistant Zinc Oxide Humans Mycobacterium tuberculosis Microbial Sensitivity Tests Drug Synergism Nanoparticles Antitubercular Agents
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A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries.

Tuberculosis (Edinburgh, Scotland) Jun 23, 2020
Tuberculosis is ranked as the 2nd deadliest disease in the world and is responsible for ten million deaths in 2017. Treatment failure is one of a main reason behind these deaths. Reasons of treatment failure are still unknown and the death rate due t...
Tuberculosis Mycobacterium tuberculosis Treatment Failure Risk Assessment Humans Support Vector Machine Neural Networks, Computer Predictive Value of Tests Machine Learning Antitubercular Agents Decision Support Techniques Reproducibility of Results Risk Factors
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