ArticlePloS one2014
Diagnostic accuracy of computer-aided detection of pulmonary tuberculosis in chest radiographs: a validation study from sub-Saharan Africa.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
52 citing papers in PubMed, 4 syntheses or guidelines pooled it, 102 citations in OpenAlex.
- Using mHealth Technologies for Case Finding in Tuberculosis and Other Infectious Diseases in Africa: Systematic Review.JMIR mHealth and uHealth · 2024Pooled it
- Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- 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 diseases : an official publication of the Infectious Diseases Society of America · 2022Pooled it
- Pooled it
- Formal and informal digital health in Tanzania: healthcare workforce, regulatory and trust implications of e-patients and virtual care and the development of the TRUST framework.Frontiers in health services · 2026Review
- Artificial intelligence in clinical toxicology in Africa: Emerging applications and barriers.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2025Review
- AI-Powered Chest X-Ray for Diagnosing Pulmonary Tuberculosis in County and Township Health Care Facilities in Yichang: Retrospective, Real-World Study.Journal of medical Internet research · 2025Article
- Performance of universal and stratified computer-aided detection thresholds for chest x-ray-based tuberculosis screening: a cross-sectional, diagnostic accuracy study.The Lancet. Digital health · 2025Article
- A systematic review and meta-analysis of artificial intelligence software for tuberculosis diagnosis using chest X-ray imaging.Journal of thoracic disease · 2025Article
- Accuracy of CAD4TB (Computer-Aided Detection for Tuberculosis) on paediatric chest radiographs.The European respiratory journal · 2024Article
- A multicentre study to evaluate the diagnostic performance of a novel CAD software, DecXpert, for radiological diagnosis of tuberculosis in the northern Indian population.Scientific reports · 2024Article
- Early detection of tuberculosis: a systematic review.Pneumonia (Nathan Qld.) · 2024Review
- Deep Learning-based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study.Journal of imaging informatics in medicine · 2024Article
- The Ubuntu Way: Ensuring Ethical AI Integration in Health Research.Wellcome open research · 2024Review
- HIV, multimorbidity, and health-related quality of life in rural KwaZulu-Natal, South Africa: A population-based study.PloS one · 2024Article
- Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru.PLOS global public health · 2024Article
- Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria.PLOS global public health · 2024Article
- Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms.ERJ open research · 2024Article
- 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 and public health · 2023Review
- Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChest radiography to diagnose and screen for pulmonary tuberculosis has limitations, especially due to inter-reader variability. Automating the interpretation has the potential to overcome this drawback and to deliver objective and reproducible results. The CAD4TB software is a computer-aided detection system that has shown promising preliminary findings. Evaluation studies in different settings are needed to assess diagnostic accuracy and practicability of use.
methodsCAD4TB was evaluated on chest radiographs of patients with symptoms suggestive of pulmonary tuberculosis enrolled in two cohort studies in Tanzania. All patients were characterized by sputum smear microscopy and culture including subsequent antigen or molecular confirmation of Mycobacterium tuberculosis (M.tb) to determine the reference standard. Chest radiographs were read by the software and two human readers, one expert reader and one clinical officer. The sensitivity and specificity of CAD4TB was depicted using receiver operating characteristic (ROC) curves, the area under the curve calculated and the performance of the software compared to the results of human readers.
resultsOf 861 study participants, 194 (23%) were culture-positive for M.tb. The area under the ROC curve of CAD4TB for the detection of culture-positive pulmonary tuberculosis was 0.84 (95% CI 0.80-0.88). CAD4TB was significantly more accurate for the discrimination of smear-positive cases against non TB patients than for smear-negative cases (p-value<0.01). It differentiated better between TB cases and non TB patients among HIV-negative compared to HIV-positive individuals (p<0.01). CAD4TB significantly outperformed the clinical officer, but did not reach the accuracy of the expert reader (p = 0.02), for a tuberculosis specific reading threshold.
conclusionCAD4TB accurately distinguished between the chest radiographs of culture-positive TB cases and controls. Further studies on cost-effectiveness, operational and ethical aspects should determine its place in diagnostic and screening algorithms.
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