AIMC Topic: Tuberculosis

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Deep learning aided quantitative analysis of anti-tuberculosis fixed-dose combinatorial formulation by terahertz spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Anti-tuberculosis fixed-dose combinatorial formulation (FDCs) is an effective drug for the treatment of tuberculosis. As a compound medicine, its efficacy is based on the comprehensive action of multiple main ingredients. If the content of an active ...

Ensemble of EfficientNets for the Diagnosis of Tuberculosis.

Computational intelligence and neuroscience
Tuberculosis (TB) remains a life-threatening disease and is one of the leading causes of mortality in developing regions due to poverty and inadequate medical resources. Tuberculosis is medicable, but it necessitates early diagnosis through reliable ...

A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning.

Journal of healthcare engineering
Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including . We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning mode...

An accurate artificial intelligence system for the detection of pulmonary and extra pulmonary Tuberculosis.

Tuberculosis (Edinburgh, Scotland)
Tuberculosis (TB) is the greatest irresistible illness in humans, caused by microbes Mycobacterium TB (MTB) bacteria and is an infectious disease that spreads from one individual to another through the air. It principally influences lung, which is te...

DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pne...

Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.

Scientific reports
Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time c...

Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study.

PloS one
BACKGROUND: Tuberculosis (TB) incidence in Los Angeles County, California, USA (5.7 per 100,000) is significantly higher than the U.S. national average (2.9 per 100,000). Directly observed therapy (DOT) is the preferred strategy for active TB treatme...

Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach.

Annals of global health
Although Artificial Intelligence (AI) is being increasingly applied, considerable distrust about introducing "disruptive" technologies persists. Intrinsic and contextual factors influencing where and how such innovations are introduced therefore requ...

Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.

European journal of nuclear medicine and molecular imaging
PURPOSE: To develop and evaluate the effectiveness of a deep learning framework (3D-ResNet) based on CT images to distinguish nontuberculous mycobacterium lung disease (NTM-LD) from Mycobacterium tuberculosis lung disease (MTB-LD).

Swarm Learning for decentralized and confidential clinical machine learning.

Nature
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an i...