ArticleThe European respiratory journal2017
An evaluation of automated chest radiography reading software for tuberculosis screening among public- and private-sector patients.
Article in The European respiratory journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- 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
- Article
- How threshold customisation affects the performance of a multiclass X-ray AI model for primary care triage: a retrospective study.BMJ open · 2026Article
- Mapping respiratory health digital interventions in South and Southeast Asia: a scoping review.Journal of global 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
- 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
- Finding the missed millions: innovations to bring tuberculosis diagnosis closer to key populations.BMC global and public health · 2024Review
- TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset.Science advances · 2024Article
- Applicability of artificial intelligence-based computer-aided detection (AI-CAD) for pulmonary tuberculosis to community-based active case finding.Tropical medicine and 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
- Comparing tuberculosis symptom screening to chest X-ray with artificial intelligence in an active case finding campaign in Northeast Nigeria.BMC global and public health · 2023Article
- Assessment of non-tuberculosis abnormalities on digital chest x-rays with high CAD4TB scores from a tuberculosis prevalence survey in Zambia and South Africa.BMC infectious diseases · 2023Article
- Review on chest pathogies detection systems using deep learning techniques.Artificial intelligence review · 2023Article
- The Performance of Computer-Aided Detection Digital Chest X-ray Reading Technologies for Triage of Active Tuberculosis Among Persons With a History of Previous Tuberculosis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis.PLOS digital health · 2022Article
- Clinical standards for the diagnosis, treatment and prevention of TB infection.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022Article
Corrections and comments
- Commented on by
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided reading (CAR) of medical images is becoming increasingly common, but few studies exist for CAR in tuberculosis (TB). We designed a prospective study evaluating CAR for chest radiography (CXR) as a triage tool before Xpert MTB/RIF (Xpert).Consecutively enrolled adults in Dhaka, Bangladesh, with TB symptoms received CXR and Xpert. Each image was scored by CAR and graded by a radiologist. We compared CAR with the radiologist for sensitivity and specificity, area under the receiver operating characteristic curve (AUC), and calculated the potential Xpert tests saved.A total of 18 036 individuals were enrolled. TB prevalence by Xpert was 15%. The radiologist graded 49% of CXRs as abnormal, resulting in 91% sensitivity and 58% specificity. At a similar sensitivity, CAR had a lower specificity (41%), saving fewer (36%) Xpert tests. The AUC for CAR was 0.74 (95% CI 0.73-0.75). CAR performance declined with increasing age. The radiologist grading was superior across all sub-analyses.Using CAR can save Xpert tests, but the radiologist's specificity was superior. Differentiated CAR thresholds may be required for different populations. Access to, and costs of, human readers must be considered when deciding to use CAR software. More studies are needed to evaluate CAR using different screening approaches.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.