• Head-to-head comparison of diagnostic accuracy of TB screening tests: Chest-X-ray, Xpert TB host response, and C-reactive protein (medRxiv, 2024)
  • An independent, multi-country head-to-head accuracy comparison of automated chest x-ray algorithms for the triage of pulmonary tuberculosis (medRxiv, 2024)
  • Computer-aided detection thresholds for digital chest x-ray interpretation in tuberculosis diagnostic algorithms (ERJ Open Research, 2024)
  • Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis (BMC Global Public Health, 2023)
  • Computer-aided detection thresholds for digital chest x-ray interpretation in tuberculosis diagnostic algorithms (ERJ Open Research, 2023)
  • Evaluation of tuberculosis diagnostic test accuracy using Bayesian latent class analysis in the presence of conditional dependence between the diagnostic tests used in a community-based tuberculosis screening study (PLOS one, 2023)
  • CAD4TB software updates: different triaging thresholds require caution by users and regulation by authorities (The International Journal of Tuberculosis and Lund Disease, 2023)
  • Evaluation of chest X-ray with automated interpretation algorithms for mass tuberculosis screening in prisons: A cross-sectional study (The Lancet Regional Health Americas, 2023)
  • Early user experience and lessons learned using ultra-portable digital X-ray with computer-aided detection (DXR-CAD) products: A qualitative study from the perspective of healthcare providers (PLOS one, 2023)
  • Economic analysis of different throughput scenarios and implementation strategies of computer-aided detection software as a screening and triage test for pulmonary TB (PLOS one, 2022)
  • Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review (Journal of Medical Systems, 2022)
  • Computer-Aided Detection of Tuberculosis from Chest Radiographs in TB Prevalence Survey: External Validation and Modelled Impacts on Commercially Available Artificial Intelligence Software (SSRN, 2022)
  • Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis (PLOS Digital Health, 2022)
  • “Similar performances but markedly different triaging thresholds in three CAD4TB versions risk systematic errors in TB screening programs” (MedRxiv, 2022)
  • Diagnostic accuracy of chest X-ray interpretation for tuberculosis by three artificial intelligence-based software in a screening use-case: an individual patient meta-analysis of global data (MedRxiv, 2022)
  • Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis (Nature Scientific Reports, 2021)
  • Costs and cost-effectiveness of a comprehensive tuberculosis case finding strategy in Zambia (PLOS one, 2021)
  • Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: and evaluation of five artificial intelligence algorithms (The Lancet Digital Health, 2021)
  • 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 (Clinical Infectious Disease, 2021)
  • Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest X-rays? An evaluation of five AI products for TB screening and triaging in a high TB burden setting (ArXiv, 2021)
  • Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening (Chronic Diseases and Translational Medicine, 2021)
  • A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers (Tuberculosis, 2021)
  • Chest X-ray Analysis with Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: a Prospective Study of Diagnostic Accuracy for Culture-Confirmed Disease (The Lancet Digital Health, 2020)
  • Can Artificial Intelligence Be Used to Accurately Detect Tuberculosis (TB) from Chest X-ray? A Multi-Platform Evaluation of Five AI Products Used for TB Screening in a High-Burden setting (ArXiv, 2020)
  • Using Artificial Intelligence to Read Chest Radiographs for Tuberculosis Detection: A Multi-Site Evaluation of the Diagnostic Accuracy of Three Deep Learning Systems (Nature Scientific Reports, 2019)
  • A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest X-rays for pulmonary tuberculosis (PLOS one, 2019)
  • Fast and Effective Quantification of Symmetry in Medical images for Pathology Detection: Application to Chest Radiography (Medical Physics, 2017)
  • Automatic Detection of Pleural Effusion in Chest Radiographs (Medical Image Analysis, 2016)
  • Computer-Aided Detection of Pulmonary Tuberculosis on Digital Chest Radiographs: a Systematic Review (The International Journal of Tuberculosis and Lung Disease, 2016)
  • On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis (IEEE Transactions on Medical Imaging, 2015)
  • Localized energy-based normalization of medical images: application to chest radiography (IEEE Transactions on Medical Imaging, 2015)
  • Automatic Detection of Tuberculosis in Chest Radiographs Using a Combination of Textural, Focal, and shape Abnormality Analysis (IEEE Transactions on Medical Imaging, 2015)
  • A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays (IEEE Transactions of Medical Imaging, 2014)
  • Cavity Contour Segmentation in Chest Radiographs using Supervised Learning and Dynamic Programming (Medical Physics, 2014)
  • Multiple-instance learning for computer-aided detection of tuberculosis (Medical Imaging, 2014)
  • Suppression of Translucent Elongated Structures: Applications in Chest Radiography (IEEE Transactions on Medical Imaging, 2013)
  • Foreign Object Detection and Removal to Improve Automated Analysis of Chest Radiographs (Medical Physics, 2013)
  • Automated Localization of Costophrenic Recesses and Costophrenic Angle Measurement on Frontal Chest Radiographs (Proceeding from SPIE Medical Imaging 2013)
  • Improved Texture Analysis for Automatic Detection of Tuberculosis (TB) on Chest Radiographs with Bone Suppression Images (Proceeding from SPIE Medical Imaging 2013)
  • Clavicle segmentation in chest radiographs (Medical Image Analysis, 2012)
  • Fusion of local and global detection systems to detect tuberculosis in chest radiographs (Medical Image Computing and Computer-Assisted Intervention,2 010)
  • Rib Suppression in Chest Radiographs to Improve Classification of Textural Abnormalities (Proceeding from SPIE Medical Imaging 2010)
  • Dissimilarity-based Classification in the Absence of Local Ground Truth: Application to the Diagnostic Interpretation of Chest Radiographs (Pattern Recognition, 2009)
  • Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography (Medical Physics, 2007)
  • Segmentation of Anatomical Structures in Chest Radiographs Using Supervised Methods: a Comparative Study on a Public Databased (Medical Image Analysis, 2006)
  • Automatic Detection of Abnormalities in Chest Radiographs Using Local Texture Analysis (IEEE Transactions on medical Imaging, 2002)


  • CAD4TB performance was stable regardless of diabetes status. [India, Madagascar, South Africa, Tanzania, the Philippines, South Africa, and Vietnam: Worodria W et al. 2024]
  • CAD4TB offers good diagnostic accuracy as triage for TB screening among diabetes patients [Habib et al., Nature Scientific Reports, 2020]
  • CAD4TB with X-ray systems allowed not only rapid and systematic triage to Xpert testing, but also found quantitatively more TB-like abnormalities in those with Diabetes Mellitus In Bangladesh [Paul et al., Science Direct, 2020]
  • CAD4TB has potential as a triage tool for TB screening in people living with diabetes, thereby significantly reducing the need for microbiological examination in Indonesia [Koesoemadinata et al., IJTLD, 2018]


Paediatric TB

  • The performance of CAD4TB v7 to identify TB in children (<13 years) significantly improved after fine-tuning it with a set of well-characterised paediatric chest x-rays. CAD has the potential to be useful additional diagnostic tool for paediatric tuberculosis. [South Africa: Palmer et al., PLOS Glob Public Health, 2023]
  • CAD may provide viable options for use in TB screening programs to increase TB detection, especially in low resource areas where there may be no available expert radiologists. [Gelaw et al., Plos Global Public Health, 2023]

Screening in Prison

  • Screening by mobile x-ray systems with automated interpretation could reduce the number of confirmatory tests required and enable screening to be more rapid in high burden TB settings, while still maintaining sufficient sensitivity. [Soares et al., The Lancet Regional Health – Americas, 2023]
  • Inclusion of digital CXR to systematic TB screening detected additional TB cases among inmates that would otherwise have been missed, and using CAD4TB may also improve performance of screening algorithm. [Kim et al., IJTLD, 2020]
  • High uptake of new screening tools such as digital X-ray with CAD4TB may be particularly feasible, reliable, and highly acceptable in prison settings. [Wali et al., BMC Public Health, 2019]
  • CAD4TB reliably evaluates CXRs from a mostly asymptomatic prison population, with a performance comparable to local readers in Tanzania. [Steiner et al., Public Health Action, 2015]

Non-TB Abnormalities

  • CAD4TB has the potential to simultaneously provide information on other non-TB abnormalities that might be of clinical relevance in communities alongside TB. CAD can be useful for LMICs where there is no routine screening for non-TB abnormalities, and there is often a shortage of qualified radiologists. [Zambia, South Africa: Ngosa, D. et al. (2023). BMC Infect Dis, 2023]


Non-TB Abnormalities

  • COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests (Nature Scientific Reports, 2023)
  • Assessment of non-tuberculosis abnormalities on digital chest x-rays with high CAD4TB scores form a tuberculosis prevalence survey in Zambia and South Africa (BMC Infectious Diseases, 2023)

CAD for TB Screening: Policies and Guidelines