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Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system

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Murphy, Keelin, Habib, Shifa Salman, Zaidi, Syed Mohammad Asad, Khowaja, Saira, Khan, Aamir, Melendez, Jaime, Scholten, Ernst T., Amad, Farhan, Schalekamp, Steven, Verhagen, Maurits, Philipsen, Rick H. H. M., Meijers, Annet, van Ginneken, Bram·2020·Scientific Reports

Abstract

There is a growing interest in the automated analysis of chest X-Ray (CXR) as a sensitive and inexpensive means of screening susceptible populations for pulmonary tuberculosis. In this work we evaluate the latest version of CAD4TB, a commercial software platform designed for this purpose. Version 6 of CAD4TB was released in 2018 and is here tested on a fully independent dataset of 5565 CXR images with GeneXpert (Xpert) sputum test results available (854 Xpert positive subjects). A subset of 500 subjects (50% Xpert positive) was reviewed and annotated by 5 expert observers independently to obtain a radiological reference standard. The latest version of CAD4TB is found to outperform all previous versions in terms of area under receiver operating curve (ROC) with respect to both Xpert and radiological reference standards. Improvements with respect to Xpert are most apparent at high sensitivity levels with a specificity of 76% obtained at a fixed 90% sensitivity. When compared with the radiological reference standard, CAD4TB v6 also outperformed previous versions by a considerable margin and achieved 98% specificity at the 90% sensitivity setting. No substantial difference was found between the performance of CAD4TB v6 and any of the various expert observers against the Xpert reference standard. A cost and efficiency analysis on this dataset demonstrates that in a standard clinical situation, operating at 90% sensitivity, users of CAD4TB v6 can process 132 subjects per day at an average cost per screen of $ 5.95 per subject, while users of version 3 process only 85 subjects per day at a cost of $ 8.38 per subject. At all tested operating points version 6 is shown to be more efficient and cost effective than any other version.

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Metadata

Authors
Murphy, K., Habib, S. S., Zaidi, S. M. A., Khowaja, S., Khan, A., Melendez, J., Scholten, E. T., Amad, F., Schalekamp, S., Verhagen, M., Philipsen, R. H. H. M., Meijers, A., & van Ginneken, B.
Affiliations
  • Radboud University Medical Center (NL)
  • Indus Hospital (PK)
Corresponding author
Murphy, Keelin
Venue
Scientific Reports
Published
2020-03-26
Volume
10
Issue
1
Pages
5492-5492
ISSN
2045-2322 (ISSN-L)
OpenAlex
W2921856303
Cited by
122
References
1
Citations by year
2026: 18 · 2025: 13 · 2024: 25 · 2023: 18 · 2022: 18 · 2021: 23 · 2020: 5
FWCI
7.79
Type
journal article
Language
en
Open access
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • arXiv (Cornell University) (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Radboud Repository (Radboud University) (submittedVersion)
  • Radboud Repository (Radboud University) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
  • Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
computer-aided detection, pulmonary tuberculosis, chest radiograph, CAD4TB, automated screening, diagnostic accuracy, cost-effectiveness, tuberculosis screening
MeSH terms
Adult, Expert Testimony, Female, Humans, Male, Middle Aged, Pakistan, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Software, Radiography, Thoracic, Tuberculosis, Pulmonary, Observer Variation, Databases, Factual, Young Adult

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