Tuberculosis

A Hybrid Transformer Architecture with a Quantized Self-Attention Mechanism Applied to Molecular Generation.

The success of the self-attention mechanism in classical machine learning models has inspired the development of quantum analogs aimed at reducing the computational overhead. Self-attention integrates learnable and matrices to calculate attention scores between all pairs of tokens in a sequence. These scores are then multiplied by a learnable matrix to obtain the output self-attention matrix, e...

Artificial Intelligence-augmented public health interventions in India.

The adoption and scaling of technology in public health settings in the Global South have traditionally been challenging. The introduction of artificial intelligence (AI) technology has exacerbated the challenges, but AI also brings with it exciting new frontiers. India is a large, diverse country that encapsulates well the challenges and opportunities for AI in the Global South. Here, we describe...

Behavior recognition technology based on deep learning used in pediatric behavioral audiometry.

This study aims to explore the feasibility and accuracy of deep learning-based pediatric behavioral audiometry. The research provides a dedicated pedi...

A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.

Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains a significant global public health threat. HIV co-infectio...

Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally.

Tuberculosis (TB) is a major global health issue, contributing significantly to mortality and morbidity rates worldwide. Socioeconomic, environmental,...

Tuberculosis detection using few shot learning.

Tuberculosis (TB), a contagious disease, significantly affects lungs functioning. Amongst multiple detection methodologies, Chest X-ray analysis is co...

Active case finding using mobile vans with artificial intelligence aided radiology tests and sputum collection for rapid diagnostic tests to reduce tuberculosis prevalence among high-risk population in rural China: Protocol for a pragmatic trial.

BACKGROUND: Tuberculosis (TB) remains a significant public health challenge, particularly in rural areas of high-burden countries like China. Active c...

Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.

Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential gene...

Deep Gated Neural Network With Self-Attention Mechanism for Survival Analysis.

Survival analysis is commonly used to model the time distributions of the first occurrences of events of interest, and it has widespread medical appli...

Performance of Computer-Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China.

BACKGROUND: Computer-aided detection (CAD) software has been introduced to automatically interpret digital chest X-rays. This study aimed to evaluate ...

A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being ...

Comparing discriminatory behavior against AI and humans.

Although discrimination is typically believed to occur from well-defined categories like ethnicity, disability, and sex, studies have found that discr...

Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.

New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Mode...

Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, especially in low-resource settings. Accurate diag...

Early differential diagnosis models of Talaromycosis and Tuberculosis in HIV-negative hosts using clinical data and machine learning.

BACKGROUND: Talaromyces marneffei is an emerging pathogen, and the number of infections in HIV-negative individuals is increasing. In HIV-negative ind...

Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.

BACKGROUND: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However...

An extensive review on infectious disease diagnosis using machine learning techniques and next generation sequencing: State-of-the-art and perspectives.

UNLABELLED: Infectious diseases, including tuberculosis (TB), HIV/AIDS, and emerging pathogens like COVID-19 pose severe global health challenges due ...

Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning.

BACKGROUND: Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating...

Artificial Intuition and accelerating the process of antimicrobial drug discovery.

New drug development is a very challenging, expensive, and usually time-consuming process. This issue is very important with regard to antimicrobials,...

The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes.

BACKGROUND: Tuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods ofte...