Evidence map›Paper›PMID 29663963›Full record

ArticleThe international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease2018

Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening.

J Melendez, L Hogeweg, C I Sánchez, R H H M Philipsen, R W Aldridge, A C Hayward, I Abubakar, B van Ginneken, A Story

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
2.3field-weighted citation impact, top 12% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.

  1. Pooled it
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  4. Review
  5. Article
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  7. Article
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  9. Tuberculosis prevention: current strategies and future directions.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2024
    Review
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  17. Identifying gaps in the quality of latent tuberculosis infection care.Journal of clinical tuberculosis and other mycobacterial diseases · 2020
    Article
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  19. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

J MelendezDiagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Thirona, Nijmegen, The Netherlands.
L HogewegDiagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
C I SánchezDiagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
R H H M PhilipsenDiagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands; Thirona, Nijmegen, The Netherlands.
R W AldridgeDepartment of Infectious Disease Informatics, Institute of Health Informatics, University College London, London, UK.
A C HaywardDepartment of Infectious Disease Informatics, Institute of Health Informatics, University College London, London, UK; Institute of Epidemiology and Health Care, University College London, UK.
I AbubakarInstitute for Global Health, University College London, UK.
B van GinnekenDiagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands; Thirona, Nijmegen, The Netherlands.
A StoryDepartment of Infectious Disease Informatics, Institute of Health Informatics, University College London, London, UK.
University College London · GBRadboud University Nijmegen · NLRadboud University Medical Center · NL

Funding

Wellcome Trust 206602/Z/17/Z
6 · The paper itself

Abstract

settingTuberculosis (TB) screening programmes can be optimised by reducing the number of chest radiographs (CXRs) requiring interpretation by human experts.

objectiveTo evaluate the performance of computerised detection software in triaging CXRs in a high-throughput digital mobile TB screening programme.

designA retrospective evaluation of the software was performed on a database of 38 961 postero-anterior CXRs from unique individuals seen between 2005 and 2010, 87 of whom were diagnosed with TB. The software generated a TB likelihood score for each CXR. This score was compared with a reference standard for notified active pulmonary TB using receiver operating characteristic (ROC) curve and localisation ROC (LROC) curve analyses.

resultsOn ROC curve analysis, software specificity was 55.71% (95%CI 55.21-56.20) and negative predictive value was 99.98% (95%CI 99.95-99.99), at a sensitivity of 95%. The area under the ROC curve was 0.90 (95%CI 0.86-0.93). Results of the LROC curve analysis were similar.

conclusionThe software could identify more than half of the normal images in a TB screening setting while maintaining high sensitivity, and may therefore be used for triage.

Indexed as

AutomationDatabases, FactualHumansMass ScreeningNetherlandsRadiography, ThoracicRetrospective StudiesROC CurveSensitivity and SpecificitySoftwareTuberculosis, Pulmonary

Identifiers

PMID29663963
PMCPMC5905390
OpenAlexW2801168973

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.