AIMC Topic: Tuberculosis
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Lite-YOLOv8: a more lightweight algorithm for Tubercle Bacilli detection.
Medical & biological engineering & computing
Sep 12, 2024
Deep learning is a transformative force in the medical field and it has made significant progress as a pivotal alternative to conventional manual testing methods. Detection of Tubercle Bacilli in sputum samples is faced with the problems of complex b...
Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing.
IEEE journal of biomedical and health informatics
Sep 5, 2024
The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-...
Fuzzy lattices assisted EJAYA Q-learning for automated pulmonary diseases classification.
Biomedical physics & engineering express
Sep 3, 2024
This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices forma...
A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection.
Scientific reports
Aug 16, 2024
TB/HIV coinfection poses a complex public health challenge. Accurate forecasting of future trends is essential for efficient resource allocation and intervention strategy development. This study compares classical statistical and machine learning mod...
Prediction of Mycobacterium tuberculosis cell wall permeability using machine learning methods.
Molecular diversity
Aug 12, 2024
Tuberculosis (TB) caused by the bacteria Mycobacterium tuberculosis (M. tb), continues to pose a significant worldwide health threat. The advent of drug-resistant strains of the disease highlights the critical need for novel treatments. The unique ce...
Machine learning investigation of tuberculosis with medicine immunity impact.
Diagnostic microbiology and infectious disease
Aug 4, 2024
Tuberculosis (T.B.) remains a prominent global cause of health challenges and death, exacerbated by drug-resistant strains such as multidrug-resistant tuberculosis MDR-TB and extensively drug-resistant tuberculosis XDR-TB. For an effective disease ma...
Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software.
The Lancet. Digital health
Jul 19, 2024
BACKGROUND: Computer-aided detection (CAD) can help identify people with active tuberculosis left undetected. However, few studies have compared the performance of commercially available CAD products for screening in high tuberculosis and high HIV se...
Geographical targeting of active case finding for tuberculosis in Pakistan using hotspots identified by artificial intelligence software (SPOT-TB): study protocol for a pragmatic stepped wedge cluster randomised control trial.
BMJ open respiratory research
Jul 11, 2024
INTRODUCTION: Pakistan has significantly strengthened its capacity for active case finding (ACF) for tuberculosis (TB) that is being implemented at scale in the country. However, yields of ACF have been lower than expected, raising concerns on its ef...
Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.
Clinical pharmacokinetics
Jul 11, 2024
INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C), as an indicator of safety and efficacy, are important for optimizing therapy.
Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.
Scientific reports
Jul 3, 2024
Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehe...
ROC Curve
Mycobacterium Infections, Nontuberculous
Biomarkers
Amino Acids
Down-Regulation
Metabolomics
Humans
Random Forest
Aged
Diagnosis, Differential
Models, Statistical
Machine Learning
Up-Regulation
Tuberculosis
Male
Area Under Curve
Liquid Chromatography-Mass Spectrometry
Phenotype
Middle Aged
Metabolome
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