Background Deep learning-based radiological image analysis could facilitate use of chest x-
rays as triage tests for pulmonary tuberculosis in resource-limited settings. We sought to …

Background Automated radiologic analysis using computer-aided detection software (CAD)
could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence …

Background Artificial intelligence (AI) algorithms can be trained to recognise tuberculosis-
related abnormalities on chest radiographs. Various AI algorithms are available …

Background The World Health Organization (WHO) recommends chest radiography to
facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise …

Deep learning (DL) neural networks have only recently been employed to interpret chest
radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No …

Background Machine learning has been an emerging tool for various aspects of infectious
diseases including tuberculosis surveillance and detection. However, the World Health …

Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet
often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis …

Computer-aided digital chest radiograph interpretation (CAD) can facilitate high-throughput
screening for tuberculosis (TB), but its use in population-based active case-finding programs …

In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active
pulmonary tuberculosis (PTB) screening when interpreted by human readers. However, they …

Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for
detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified …