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Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa

BACKGROUND: Chest 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-...

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Autores principales: Breuninger, Marianne, van Ginneken, Bram, Philipsen, Rick H. H. M., Mhimbira, Francis, Hella, Jerry J., Lwilla, Fred, van den Hombergh, Jan, Ross, Amanda, Jugheli, Levan, Wagner, Dirk, Reither, Klaus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4156349/
https://www.ncbi.nlm.nih.gov/pubmed/25192172
http://dx.doi.org/10.1371/journal.pone.0106381
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author Breuninger, Marianne
van Ginneken, Bram
Philipsen, Rick H. H. M.
Mhimbira, Francis
Hella, Jerry J.
Lwilla, Fred
van den Hombergh, Jan
Ross, Amanda
Jugheli, Levan
Wagner, Dirk
Reither, Klaus
author_facet Breuninger, Marianne
van Ginneken, Bram
Philipsen, Rick H. H. M.
Mhimbira, Francis
Hella, Jerry J.
Lwilla, Fred
van den Hombergh, Jan
Ross, Amanda
Jugheli, Levan
Wagner, Dirk
Reither, Klaus
author_sort Breuninger, Marianne
collection PubMed
description BACKGROUND: Chest 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. METHODS: CAD4TB 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. RESULTS: Of 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. CONCLUSION: CAD4TB 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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spelling pubmed-41563492014-09-09 Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa Breuninger, Marianne van Ginneken, Bram Philipsen, Rick H. H. M. Mhimbira, Francis Hella, Jerry J. Lwilla, Fred van den Hombergh, Jan Ross, Amanda Jugheli, Levan Wagner, Dirk Reither, Klaus PLoS One Research Article BACKGROUND: Chest 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. METHODS: CAD4TB 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. RESULTS: Of 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. CONCLUSION: CAD4TB 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. Public Library of Science 2014-09-05 /pmc/articles/PMC4156349/ /pubmed/25192172 http://dx.doi.org/10.1371/journal.pone.0106381 Text en © 2014 Breuninger et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Breuninger, Marianne
van Ginneken, Bram
Philipsen, Rick H. H. M.
Mhimbira, Francis
Hella, Jerry J.
Lwilla, Fred
van den Hombergh, Jan
Ross, Amanda
Jugheli, Levan
Wagner, Dirk
Reither, Klaus
Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa
title Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa
title_full Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa
title_fullStr Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa
title_full_unstemmed Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa
title_short Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa
title_sort diagnostic accuracy of computer-aided detection of pulmonary tuberculosis in chest radiographs: a validation study from sub-saharan africa
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4156349/
https://www.ncbi.nlm.nih.gov/pubmed/25192172
http://dx.doi.org/10.1371/journal.pone.0106381
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