Cited by 26Tuberculosis Research and EpidemiologyLung Cancer Diagnosis and TreatmentCOVID-19 diagnosis using AI

The DOI matches its Crossref record.

Automated chest radiography and mass systematic screening for tuberculosis

Madhani, F., Maniar, R. A., Burfat, A., Ahmed, M., Farooq, S., Sabir, A., Domki, A. K., Page-Shipp, L., Khowaja, S., Safdar, N., Khan, A. J., Khan, P. Y.·2020·The International Journal of Tuberculosis and Lung Disease

Abstract

BACKGROUND: Systematic screening for TB using automated chest radiography (ACR) with computer-aided detection software (CAD4TB) has been implemented at scale in Karachi, Pakistan. Despite evidence supporting the use of ACR as a pre-screen prior to Xpert® MTB/RIF diagnostic testing in presumptive TB patients, there has been no data published on its use in mass screening in real-world settings.METHOD: Screening was undertaken using mobile digital X-ray vehicles at hospital facilities and community camps. Chest X-rays were offered to individuals aged ≥15 years, regardless of symptoms. Those with a CAD4TB score of ≥70 were offered Xpert testing. The association between Xpert positivity and CAD4TB scores was examined using data collected between 1 January and 30 June 2018 using a custom-built data collection tool.RESULTS: Of the 127 062 individuals screened, 97.2% had a valid CAD4TB score; 11 184 (9.1%) individuals had a CAD4TB score ≥70. Prevalence of Xpert positivity rose from 0.7% in the <50 category to 23.5% in the >90 category. The strong linear association between CAD4TB score and Xpert positivity was found in both community and hospital settings.CONCLUSION: The strong association between CAD4TB scores and Xpert positivity provide evidence that an ACR-based pre-screening performs well when implemented at scale in a high-burden setting.

Overview

Integrity signals

Registry

Crossref · 10.5588/ijtld.19.0501

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cited by

Sorted by citations

See all 26

Metadata

Authors
Madhani, F., Maniar, R. A., Burfat, A., Ahmed, M., Farooq, S., Sabir, A., Domki, A. K., Page-Shipp, L., Khowaja, S., Safdar, N., Khan, A. J., & Khan, P. Y.
Affiliations
  • Indus Hospital (PK)
  • Provincial TB Control Program, Department of Health, Government of Sindh, Hyderabad, Pakistan
  • Interactive Research and Development, Singapore
  • Interactive Research and Development (PK)
  • London School of Hygiene & Tropical Medicine (GB)
Venue
The International Journal of Tuberculosis and Lung Disease
Published
2020-07-01
Volume
24
Issue
7
Pages
665-673
ISSN
1027-3719 (ISSN-L)
OpenAlex
W3046196624
Cited by
26
References
0
Citations by year
2025: 1 · 2024: 5 · 2023: 6 · 2022: 6 · 2021: 7 · 2020: 1
FWCI
0.66
Type
journal article
Language
en
Open access
closed
Locations
  • The International Journal of Tuberculosis and Lung Disease (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Keywords
tuberculosis screening, computer-aided detection, CAD4TB, Xpert MTB/RIF, mass screening, prescreening, tuberculosis prevalence, high-burden settings
MeSH terms
Aged, Humans, Mass Screening, Mycobacterium tuberculosis, Pakistan, Radiography, Sensitivity and Specificity, Tuberculosis, Tuberculosis, Pulmonary

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review