Tuberculosis

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.

Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and comput...

From CNN to Transformer: A Review of Medical Image Segmentation Models.

Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. The use of deep learning for image segmentation has become a prevalent trend. The widely adopted approach currently is U-Net and its variants. Moreover, with the remarkable success of pre-trained models in natural language processing tasks, ...

Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn's Disease and Intestinal Tuberculosis.

This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn's disease (CD) and intestinal tube...

PulmoNet: a novel deep learning based pulmonary diseases detection model.

Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals ...

Clinical utilization of artificial intelligence in predicting therapeutic efficacy in pulmonary tuberculosis.

Traditional methods for monitoring pulmonary tuberculosis (PTB) treatment efficacy lack sensitivity, prompting the exploration of artificial intellige...

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA).

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. T...

Application of artificial intelligence in diagnosis of pulmonary tuberculosis.

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.

The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) in...

Deep learning model to differentiate Crohn's disease from intestinal tuberculosis using histopathological whole slide images from intestinal specimens.

Crohn's disease (CD) and intestinal tuberculosis (ITB) share similar histopathological characteristics, and differential diagnosis can be a dilemma fo...

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

Chest radiographs are examined in typical clinical settings by competent physicians for tuberculosis diagnosis. However, this procedure is time consum...

Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis.

The integration of artificial intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly...

A bipolar intuitionistic fuzzy decision-making model for selection of effective diagnosis method of tuberculosis.

OBJECTIVES: Tuberculosis (TB) is a contagious illness caused by Mycobacterium tuberculosis. The initial symptoms of TB are similar to other respirator...

Artificial intelligence-based screening for amblyopia and its risk factors: comparison with four classic stereovision tests.

INTRODUCTION: The development of costs-effective and sensitive screening solutions to prevent amblyopia and identify its risk factors (strabismus, ref...

Investigating Novel IspE Inhibitors of the MEP Pathway in Mycobacterium.

In a recent effort to mitigate harm from human pathogens, many biosynthetic pathways have been extensively evaluated for their ability to inhibit path...

Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis.

Despite 30 years as a public health emergency, tuberculosis (TB) remains one of the world's deadliest diseases. Most deaths are among persons with TB ...

MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations.

Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting gra...

A Vision for the Future of Astrochemistry in the Interstellar Medium by 2050.

By 2050, many, but not nearly all, unattributed astronomical spectral features will be conclusively linked to molecular carriers (as opposed to nearly...

Strengthening the Diagnosis of Drug-Resistant Tuberculosis Using NGS-Based Approaches and Bioinformatics Pipelines for Data Analysis in India.

In India, drug-resistant tuberculosis (DR-TB) is a major public health issue and a significant challenge to stop TB program. An estimated 27% of new T...

Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning.

INTRODUCTION: This study aimed to develop and assess a deep-learning model based on CT images for distinguishing infectivity in patients with pulmonar...

Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning.

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 tuberc...