Tuberculosis

Understanding Providers' Attitude Toward AI in India's Informal Health Care Sector: Survey Study.

BACKGROUND: Tuberculosis (TB) is a major global health concern, causing 1.5 million deaths in 2020. Diagnostic tests for TB are often inaccurate, expensive, and inaccessible, making chest x-rays augmented with artificial intelligence (AI) a promising solution. However, whether providers are willing to adopt AI is not apparent.

ResGloTBNet: An interpretable deep residual network with global long-range dependency for tuberculosis screening of sputum smear microscopy images.

Tuberculosis is a high-mortality infectious disease. Manual sputum smear microscopy is a common and effective method for screening tuberculosis. However, it is time-consuming, labor-intensive, and has low sensitivity. In this study, we propose ResGloTBNet, a framework that integrates convolutional neural network and graph convolutional network for sputum smear image classification with high discri...

Artificial intelligence: a useful tool in active tuberculosis screening among vulnerable groups in Romania - advantages and limitations.

INTRODUCTION: Despite advances in diagnostic technologies for tuberculosis (TB), global control of this disease requires improved technologies for act...

Deep learning-driven bacterial cytological profiling to determine antimicrobial mechanisms in .

Tuberculosis (TB), caused by , remains a significant global health threat, affecting an estimated 10.6 million people in 2022. The emergence of multid...

The TB27 Transcriptomic Model for Predicting Culture Conversion.

RATIONALE: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of . Recently, host RNA signatures have been used to mon...

Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.

Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence...

Current update on surgical management for spinal tuberculosis: a scientific mapping of worldwide publications.

INTRODUCTION: Spinal tuberculosis (TB), or Pott's disease, remains a significant global health issue, particularly in regions with high TB prevalence....

Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.

PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs i...

Comparative diagnostic accuracy of ChatGPT-4 and machine learning in differentiating spinal tuberculosis and spinal tumors.

BACKGROUND: In clinical practice, distinguishing between spinal tuberculosis (STB) and spinal tumors (ST) poses a significant diagnostic challenge. Th...

Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray.

BACKGROUND:: Tuberculosis disease is the disease that causes significant morbidity and mortality worldwide. Thus, early detection of the disease is cr...

A novel non-invasive murine model for rapidly testing drug activity via inhalation administration against .

The efficacy of many compounds against is often limited when administered via conventional oral or injection routes due to suboptimal pharmacokinetic...

Deep learning-based metabolomics data study of prostate cancer.

As a heterogeneous disease, prostate cancer (PCa) exhibits diverse clinical and biological features, which pose significant challenges for early diagn...

Mask R-CNN assisted diagnosis of spinal tuberculosis.

The prevalence of spinal tuberculosis (ST) is particularly high in underdeveloped regions with inadequate medical conditions. This not only leads to m...

Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.

BACKGROUND: This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in pati...

Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.

BACKGROUND: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concer...

MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials.

The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-perfo...

Diagnostic Accuracy of Artificial Intelligence-Based Chest X-Ray reading for screening of Tuberculosis.

BACKGROUND: Tuberculosis remains a public health challenge in Nepal and ranks as the seventh leading cause of death in the country. The END Tuberculos...

Rapid Deployment of Antiviral Drugs Using Single-Virus Tracking and Machine Learning.

The outbreak of emerging acute viral diseases urgently requires the acceleration of specialized antiviral drug development, thus widely adopting pheno...

Efficacy evaluation of a minimally invasive surgical procedure (oblique lateral interbody fusion) for lumbar spinal tuberculosis-retrospective cohort study.

OBJECTIVE: In the current study, to demonstrate the advantages of oblique lateral interbody fusion (OLIF), we focused on the therapeutics for lumbar s...

Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning.

Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the le...