Computer-Aided Detection for Chest Radiography to Improve the Quality of Tuberculosis Diagnosis in Vietnam’s District Health Facilities: An Implementation Study

CAD4TB software updates: different triaging thresholds require caution by users and regulation by authorities

(2023) J. Fehr et al.   INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE

The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination

(2023) C. Geric et al.   INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE

Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges

(2023) Salvatore Claudio Fanni et al.   Diagnostics

Chest X-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: an individual patient data meta-analysis of diagnostic accuracy

(2021) Gamuchirai Tavaziva et al.   CLINICAL INFECTIOUS DISEASES

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations

(2020) Michael P. Recht et al.   EUROPEAN RADIOLOGY

Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India

(2020) Madlen Nash et al.   Scientific Reports

Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system

(2020) Keelin Murphy et al.   Scientific Reports

Automated chest radiography and mass systematic screening for tuberculosis

(2020) F. Madhani et al.   INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE

Correction: The second national tuberculosis prevalence survey in Vietnam

(2020) Hai Viet Nguyen et al.   PLoS One

Artificial intelligence for precision education in radiology

(2019) Michael Tran Duong et al.   BRITISH JOURNAL OF RADIOLOGY

Automated chest X-ray reading for tuberculosis in the Philippines to improve case detection: a cohort study

(2019) R. H. H. M. Philipsen et al.   INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE

A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis

(2019) Miriam Harris et al.   PLoS One

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers

(2019) Bernardo C. Bizzo et al.   Journal of the American College of Radiology

Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems

(2019) Zhi Zhen Qin et al.   Scientific Reports

Deep learning in chest radiography: Detection of findings and presence of change

(2018) Ramandeep Singh et al.   PLoS One

The long and winding road of chest radiography for tuberculosis detection

(2017) Cecily Miller et al.   EUROPEAN RESPIRATORY JOURNAL

Precrec: fast and accurate precision–recall and ROC curve calculations in R

(2016) Takaya Saito et al.   BIOINFORMATICS

The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

(2015) Takaya Saito et al.   PLoS One

The Sensitivity and Specificity of Using a Computer Aided Diagnosis Program for Automatically Scoring Chest X-Rays of Presumptive TB Patients Compared with Xpert MTB/RIF in Lusaka Zambia

(2014) Monde Muyoyeta et al.   PLoS One

Target prioritization and strategy selection for active case-finding of pulmonary tuberculosis: a tool to support country-level project planning

(2013) Nobuyuki Nishikiori et al.   BMC PUBLIC HEALTH

pROC: an open-source package for R and S+ to analyze and compare ROC curves

(2011) Xavier Robin et al.   BMC BIOINFORMATICS

National survey of tuberculosis prevalence in Viet Nam

(2010) Nguyen Binh Hoa et al.   BULLETIN OF THE WORLD HEALTH ORGANIZATION