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Tuberculosis

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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
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...

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)
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...

A systematic review and repeatability study on the use of deep learning for classifying and detecting tuberculosis bacilli in microscopic images.

Progress in biophysics and molecular biology
Tuberculosis (TB) is among the leading causes of death worldwide from a single infectious agent. This disease usually affects the lungs (pulmonary TB) and can be cured in most cases with a quick diagnosis and proper treatment. Microscopic sputum smea...

Leptin levels in childhood tuberculosis and its correlation with body mass index, IFN-γ, and TNF-α in an Indonesian population.

The Indian journal of tuberculosis
BACKGROUND: Leptin plays a key role in the regulation of energy and inflammation in tuberculosis (TB). However, its correlation in children with TB remains unclear. Therefore, this study aimed to evaluate the correlations between body mass index, IFN...

Identification of Active Pulmonary Tuberculosis Among Patients With Positive Interferon-Gamma Release Assay Results: Value of a Deep Learning-based Computer-aided Detection System in Different Scenarios of Implementation.

Journal of thoracic imaging
PURPOSE: To evaluate the accuracy of a deep learning-based computer-aided detection (CAD) system in identifying active pulmonary tuberculosis on chest radiographs (CRs) of patients with positive interferon-gamma release assay (IGRA) results in differ...

Artificial Intelligence in Paediatric Tuberculosis.

Pediatric radiology
Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus...

Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach.

Scientific reports
Chest X-rays are the most economically viable diagnostic imaging test for active pulmonary tuberculosis screening despite the high sensitivity and low specificity when interpreted by clinicians or radiologists. Computer aided detection (CAD) algorith...

Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.

Journal of medical systems
There has been an explosive growth in research over the last decade exploring machine learning techniques for analyzing chest X-ray (CXR) images for screening cardiopulmonary abnormalities. In particular, we have observed a strong interest in screeni...

Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI.

Computers in biology and medicine
Chest X-ray (CXR) images are considered useful to monitor and investigate a variety of pulmonary disorders such as COVID-19, Pneumonia, and Tuberculosis (TB). With recent technological advancements, such diseases may now be recognized more precisely ...

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.

Computational intelligence and neuroscience
Tuberculosis (TB) is an airborne disease caused by . It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased ...