AIMC Topic: Tuberculosis

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Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs.

BMC medical imaging Feb 5, 2024
Chest radiographs are examined in typical clinical settings by competent physicians for tuberculosis diagnosis. However, this procedure is time consuming and subjective. Due to the growing usage of machine learning techniques in applied sciences, res...
Mass Screening Radiography Tuberculosis Bayes Theorem Humans Machine Learning
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Retrospective validation of MetaSystems' deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria.

Journal of clinical microbiology Feb 1, 2024
UNLABELLED: This study aimed to validate Metasystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module (Neon Metafer) with assistance on respiratory and pleural samples, compared to conventiona...
Deep Learning Sputum Artificial Intelligence Retrospective Studies Mycobacterium tuberculosis Mycobacterium Neon Microscopy, Fluorescence Tuberculosis Humans
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A bipolar intuitionistic fuzzy decision-making model for selection of effective diagnosis method of tuberculosis.

Acta tropica Jan 26, 2024
OBJECTIVES: Tuberculosis (TB) is a contagious illness caused by Mycobacterium tuberculosis. The initial symptoms of TB are similar to other respiratory illnesses, posing diagnostic challenges. Therefore, the primary goal of this study is to design a ...
Humans Algorithms Fuzzy Logic Tuberculosis
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Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning.

Biochemical genetics Nov 22, 2023
The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coin...
Transcriptome Protein Interaction Maps SARS-CoV-2 Gene Expression Profiling Tuberculosis Computational Biology Coinfection Humans Machine Learning COVID-19
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Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep learning algorithm.

Frontiers in cellular and infection microbiology Nov 1, 2023
INTRODUCTION: Active tuberculosis (ATB), instigated by Mycobacterium tuberculosis (M.tb), rises as a primary instigator of morbidity and mortality within the realm of infectious illnesses. A significant portion of M.tb infections maintain an asymptom...
Tuberculosis Latent Tuberculosis Deep Learning Pyroptosis Humans Algorithms
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Deep learning and radiomics of longitudinal CT scans for early prediction of tuberculosis treatment outcomes.

European journal of radiology Oct 30, 2023
BACKGROUND: To predict tuberculosis (TB) treatment outcomes at an early stage, prevent poor outcomes ofdrug-resistant tuberculosis(DR-TB) and interrupt transmission.
Treatment Outcome Tomography, X-Ray Computed Tuberculosis Area Under Curve Deep Learning Humans Retrospective Studies
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Localization and phenotyping of tuberculosis bacteria using a combination of deep learning and SVMs.

Computers in biology and medicine Oct 13, 2023
Successful treatment of pulmonary tuberculosis (TB) depends on early diagnosis and careful monitoring of treatment response. Identification of acid-fast bacilli by fluorescence microscopy of sputum smears is a common tool for both tasks. Microscopy-b...
Mycobacterium tuberculosis Microscopy, Fluorescence Tuberculosis Deep Learning Lipids Humans Sensitivity and Specificity
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Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

Journal of medical Internet research Jul 3, 2023
BACKGROUND: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts rea...
Deep Learning Radiography Tuberculosis X-Rays COVID-19 Humans Reproducibility of Results Artificial Intelligence
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Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

Thoracic cancer May 14, 2023
BACKGROUND: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model h...
Humans Feasibility Studies Positron Emission Tomography Computed Tomography Deep Learning Tuberculosis Reproducibility of Results Lung Neoplasms
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Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis: Effects of moving health technologies in global health.

Social science & medicine (1982) May 3, 2023
Computer Aided Detection software based on Artificial Intelligence (AI-CAD), combined with chest X-rays have recently been promoted as an easy fix for a complex problem: ending TB by 2030. WHO has recommended the use of such imaging devices in 2021 a...
Computers Humans Tuberculosis Artificial Intelligence
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