Tuberculosis

Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents.

Tuberculosis (TB) is an infectious disease and the leading cause of death globally. The rapidly emerging cases of drug resistance among pathogenic mycobacteria have been a global threat urging the need of new drug discovery and development. However, considering the fact that the new drug discovery and development is commonly lengthy and costly processes, strategic use of the cutting-edge machine l...

Automated detection of Mycobacterium tuberculosis using transfer learning.

INTRODUCTION: Quantitative analysis of Mycobacterium tuberculosis using microscope is very critical for diagnosing tuberculosis diseases. Microbiologist encounter several challenges which can lead to misdiagnosis. However, there are 3 main challenges: (1) The size of Mycobacterium tuberculosis is very small and difficult to identify as a result of low contrast background, heterogenous shape, irreg...

Swarm Learning for decentralized and confidential clinical machine learning.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be ...

Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice.

BACKGROUND: Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few effo...

Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method.

Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing...

Deep-learning based repurposing of FDA-approved drugs against dihydrofolate reductase and molecular dynamics study.

causes the fatal fungal bloodstream infection in humans called Candidiasis. Most of the species are resistant to the antifungals used to treat them....

Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT.

OBJECTIVES: Cervical lymph nodes with internal cystic changes are seen with several pathologies, including papillary thyroid carcinoma (PTC), tubercul...

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach.

To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represe...

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.

BACKGROUND AND OBJECTIVES: Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time...

Automatic phenotyping of electronical health record: PheVis algorithm.

Electronic Health Records (EHRs) often lack reliable annotation of patient medical conditions. Phenorm, an automated unsupervised algorithm to identif...

Prediction of the duration needed to achieve culture negativity in patients with active pulmonary tuberculosis using convolutional neural networks and chest radiography.

BACKGROUND: We aimed to predict the duration needed to achieve culture negativity in patients with active pulmonary tuberculosis using convolutional n...

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

BACKGROUND: Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electron...

Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images.

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 inf...

Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning.

To assist physicians identify COVID-19 and its manifestations through the automatic COVID-19 recognition and classification in chest CT images with de...

Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.

BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic...

Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain.

In the United Kingdom, despite decades of control efforts, bovine tuberculosis (bTB) has not been controlled and currently costs ~ £100 m annually. Cr...

Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors.

Tuberculosis (TB) remains one of the major health problems in modern times with a high mortality rate. While efforts are being made to make early diag...

A comparison of the value of two machine learning predictive models to support bovine tuberculosis disease control in England.

Nearly a decade into Defra's current eradication strategy, bovine tuberculosis (bTB) remains a serious animal health problem in England, with c.30,000...

A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers.

Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities fr...

The diagnosis of tuberculous meningitis in adults and adolescents: protocol for a systematic review and individual patient data meta-analysis to inform a multivariable prediction model.

Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor ...