AIMC Topic: Tuberculosis

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Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

European radiology Jun 27, 2020
OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (...
Logistic Models Nomograms Age Factors Solitary Pulmonary Nodule Regression Analysis Deep Learning ROC Curve Image Processing, Computer-Assisted Lung Neoplasms Diagnostic Tests, Routine Humans Observer Variation Algorithms Pattern Recognition, Automated Retrospective Studies Calibration Tomography, X-Ray Computed Adenocarcinoma of Lung Male Diagnosis, Differential Middle Aged Granuloma Diagnosis, Computer-Assisted Tuberculosis Female Sex Factors Adult
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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...
Humans Neural Networks, Computer Machine Learning Decision Support Techniques Reproducibility of Results Antitubercular Agents Risk Factors Tuberculosis Risk Assessment Mycobacterium tuberculosis Support Vector Machine Treatment Failure Predictive Value of Tests
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Forecasting tuberculosis using diabetes-related google trends data.

Pathogens and global health May 26, 2020
Online activity-based data can be used to aid infectious disease forecasting. Our aim was to exploit the converging nature of the tuberculosis (TB) and diabetes epidemics to forecast TB case numbers. Thus, we extended TB prediction models based on tr...
Neural Networks, Computer Machine Learning Forecasting Diabetes Mellitus Humans Tuberculosis Germany Epidemics Epidemiological Monitoring
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Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques.

Computer methods and programs in biomedicine Dec 27, 2019
BACKGROUND: The study compared the predictive outcomes of artificial neural network, support vector machine and random forest on the occurrence of anti-tuberculosis drug-induced hepatotoxicity.
Gene Frequency Polymorphism, Genetic Chemical and Drug Induced Liver Injury Drug Discovery ROC Curve Genomics Genotype Taiwan Antitubercular Agents Tuberculosis Support Vector Machine Liver Adult Sensitivity and Specificity Male Middle Aged Reproducibility of Results Risk Factors Aged Aged, 80 and over Neural Networks, Computer Machine Learning Female Humans
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Artificial Intelligence, Radiology, and Tuberculosis: A Review.

Academic radiology Nov 20, 2019
Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advance...
Artificial Intelligence Diagnosis, Computer-Assisted Radiography Radiology Tuberculosis Humans
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ATBdiscrimination: An in Silico Tool for Identification of Active Tuberculosis Disease Based on Routine Blood Test and T-SPOT.TB Detection Results.

Journal of chemical information and modeling Oct 25, 2019
Tuberculosis remains one of the deadliest infectious diseases worldwide. Only 5-15% of people infected with develop active TB disease (ATB), while others remain latently infected (LTBI) during their lifetime, which has a completely different clinica...
Humans Latent Tuberculosis Hematologic Tests Machine Learning Software Computer Simulation Tuberculosis Mycobacterium tuberculosis
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Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms.

Respiratory research Oct 16, 2019
BACKGROUND: The differential diagnosis of tuberculous pleural effusion (TPE) is challenging. In recent years, artificial intelligence (AI) machine learning algorithms have started being used to an increasing extent in disease diagnosis due to the hig...
Prospective Studies Retrospective Studies Female Aged Middle Aged Pleural Effusion Tuberculosis Adult Male Humans Artificial Intelligence Machine Learning Algorithms
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Perceptions of drones, digital adherence monitoring technologies and educational videos for tuberculosis control in remote Madagascar: a mixed-method study protocol.

BMJ open May 9, 2019
INTRODUCTION: Poor road and communication infrastructure pose major challenges to tuberculosis (TB) control in many regions of the world. TB surveillance and patient support often fall to community health workers (CHWs) who may lack the time or knowl...
Clinical Protocols Health Education Adult Attitude to Health Cross-Sectional Studies Madagascar Male Interviews as Topic Humans Female Telemedicine Video Recording Perception Focus Groups Medication Adherence Rural Health Services Robotics Antitubercular Agents Tuberculosis
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Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization.

Scientific reports Apr 18, 2019
Automated diagnosis of tuberculosis (TB) from chest X-Rays (CXR) has been tackled with either hand-crafted algorithms or machine learning approaches such as support vector machines (SVMs) and convolutional neural networks (CNNs). Most deep neural net...
Thorax Neural Networks, Computer X-Rays Machine Learning Deep Learning Algorithms Humans Support Vector Machine Image Processing, Computer-Assisted Databases, Factual Radiography Tuberculosis
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Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images.

PloS one Feb 27, 2019
Tuberculosis is an infectious disease that causes ill health and death in millions of people each year worldwide. Timely diagnosis and treatment is key to full patient recovery. The Microscopic Observed Drug Susceptibility (MODS) is a test to diagnos...
Tuberculosis Humans Microscopy Neural Networks, Computer Automation Image Processing, Computer-Assisted Antitubercular Agents
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