AIMC Topic: Tuberculosis, Pulmonary
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Pre-trained convolutional neural networks as feature extractors for tuberculosis detection.
Computers in biology and medicine
Aug 4, 2017
It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most adva...
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.
Radiology
Apr 24, 2017
Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified HIPAA-compliant datasets were used in this study that were exempted from revi...
Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients.
Antimicrobial agents and chemotherapy
Sep 23, 2016
Aminoglycosides such as amikacin continue to be part of the backbone of treatment of multidrug-resistant tuberculosis (MDR-TB). We measured amikacin concentrations in 28 MDR-TB patients in Botswana receiving amikacin therapy together with oral levofl...
Treatment Outcome
Regression Analysis
Antitubercular Agents
Tuberculosis, Multidrug-Resistant
Middle Aged
Artificial Intelligence
Botswana
Tuberculosis, Pulmonary
Microbial Sensitivity Tests
Female
Humans
Young Adult
Mycobacterium tuberculosis
Adolescent
Aged
Adult
Amikacin
Models, Theoretical
Aged, 80 and over
Male
Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil.
Medical & biological engineering & computing
Mar 25, 2016
Pulmonary tuberculosis (PTB) remains a worldwide public health problem. Diagnostic algorithms to identify the best combination of diagnostic tests for PTB in each setting are needed for resource optimization. We developed one artificial neural networ...
Factors contribute to efficiency of specimen concentration of Mycobacterium tuberculosis by centrifugation and magnetic beads.
International journal of mycobacteriology
Jun 25, 2015
BACKGROUND: A concentration of specimen is recommended for the effective recovery of Mycobacterium tuberculosis (MTB), but the bacteriological efficiency is not well evaluated. The present study evaluated the factors contributing to concentration eff...
Deep Learning Models for CT Segmentation of Invasive Pulmonary Aspergillosis, Mucormycosis, Bacterial Pneumonia and Tuberculosis: A Multicentre Study.
Mycoses
Jul 1, 2025
BACKGROUND: The differential diagnosis of invasive pulmonary aspergillosis (IPA), pulmonary mucormycosis (PM), bacterial pneumonia (BP) and pulmonary tuberculosis (PTB) are challenging due to overlapping clinical and imaging features. Manual CT lesio...
A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.
Nursing & health sciences
Mar 1, 2025
This systematic review evaluates the application of machine learning (ML) models for diagnosing pulmonary tuberculosis and their potential to inform nursing practice and implementation strategies. Studies published between 2019 and 2024 were systemat...
An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.
Journal of medical primatology
Aug 1, 2024
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,...
Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Apr 28, 2022
BACKGROUND: Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD...
Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms.
The Lancet. Digital health
Sep 1, 2021
BACKGROUND: Artificial intelligence (AI) algorithms can be trained to recognise tuberculosis-related abnormalities on chest radiographs. Various AI algorithms are available commercially, yet there is little impartial evidence on how their performance...
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