Tuberculosis

Zoonoticus: A machine learning model for genomic prediction of zoonotic bacterial strains using virulence, resistance, and mobile genetic elements.

Zoonotic diseases continue to rise globally, yet no existing genomic tool integrates virulence, antimicrobial resistance (AMR), and mobile genetic elements to predict zoonotic potential. Here, we present Zoonoticus, a machine learning-based model that classifies bacterial strains as zoonotic or non-zoonotic using whole-genome data. The model was developed using a curated reference database of 37,2...

Machine learning approaches to predict drug resistance in tuberculosis.

Tuberculosis (TB) remains a global health crisis, with 10.8 million cases and 1.25 million deaths in 2023. The rise of drug-resistant TB has complicated treatment, while traditional diagnostic methods face limitations in speed, cost, and accuracy. This study explores machine learning (ML) models to predict drug resistance from genomic variants, offering a faster and more comprehensive solution. We...

Improving the performance of the echinococcosis diagnosis model based on serum Raman spectroscopy via the integration of convolutional neural network and support vector machine.

Echinococcosis is a zoonotic parasitic disease characterized by its insidious nature and severe health impacts. Rapid and accurate screening is crucia...

Lightweight self-attention and deep gated neural network (LSA-DGNet) for multiple neurological disease detection.

Detecting neurological diseases is an important task in modern medicine, for which it is crucial to accurately model the temporal distributions of dis...

Algal/bacterial membrane bioreactor for bioremediation of hazardous landfill leachate containing 1,4-dioxane: An artificial neural network modeling.

This study developed an artificial neural network (ANN) model to predict the 1,4-dioxane removal efficiency from hazardous landfill leachate treated b...

Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.

BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resi...

Advancing Tuberculosis Screening: The Clinical Potential of Artificial Intelligence-Generated Radiology Reports for Chest Radiographs.

Graph-based vision transformer with sparsity for training on small datasets from scratch.

Vision Transformers (ViTs) have achieved impressive results in large-scale image classification. However, when training from scratch on small datasets...

Unlocking potent anti-tuberculosis natural products through structure-activity relationship analysis.

Tuberculosis (TB) remains a world health problem due to the high number of affected individuals, high mortality rates, prolonged treatment durations, ...

Exploring tuberculosis patients' preferences for AI-assisted remote health management services in China: a protocol for a discrete choice experiment.

INTRODUCTION: Effective health management is critical for patients with tuberculosis (TB), especially given the need for long-term treatment adherence...

Advancements in Tuberculosis Diagnostics: An Update.

Tuberculosis (TB) is one of the major life-threatening diseases caused by a single pathogen which has become a social menace owing to its high resista...

Disease Classification of Pulmonary Xenon Ventilation MRI Using Artificial Intelligence.

RATIONALE AND OBJECTIVES: Hyperpolarized Xenon magnetic resonance imaging (MRI) measures the extent of lung ventilation by ventilation defect percent ...

Development and validation of a deep learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis a multicenter study.

BACKGROUND: To develop and validate an ensemble machine learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis ...

Multimodal Generative Artificial Intelligence Model for Creating Radiology Reports for Chest Radiographs in Patients Undergoing Tuberculosis Screening.

Chest radiographs play a crucial role in tuberculosis screening in high-prevalence regions, although widespread radiographic screening requires exper...

Predictive modelling of air pollution affecting human tuberculosis risk on Mainland China.

In this study, we investigated the correlation between air pollution indicators and pulmonary tuberculosis (TB) incidence and mortality rates across p...

Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures.

BACKGROUND: Tuberculosis (TB) remains a significant global health challenge, as current diagnostic methods are often resource-intensive, time-consumin...

Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.

Periodontitis, a chronic inflammatory condition of the periodontium, is associated with over 60 systemic diseases. Despite advancements, precision med...

Forecasting tuberculosis in Ethiopia using deep learning: progress toward sustainable development goal evidence from global burden of disease 1990-2021.

BACKGROUND: Tuberculosis (TB) is a preventable and treatable disease caused by Mycobacterium tuberculosis, which most often affects lungs and remains ...

Multiomics and Machine Learning Identify Immunometabolic Biomarkers for Active Tuberculosis Diagnosis Against Nontuberculous Mycobacteria and Latent Tuberculosis Infection.

This study utilized multiomics combined with a comprehensive machine learning-based predictive modeling approach to identify, validate, and prioritize...

Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles.

Once deployed, medical image analysis methods are often faced with unexpected image corruptions and noise perturbations. These unknown covariate shift...