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Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children
Diagnostic tools for paediatric tuberculosis remain limited, with heavy reliance on clinical algorithms which include chest x-ray. Computer aided detection (CAD) for tuberculosis on chest x-ray has shown promise in adults. We aimed to measure and optimise the performance of an adult CAD system, CAD4...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187911/ https://www.ncbi.nlm.nih.gov/pubmed/37192175 http://dx.doi.org/10.1371/journal.pgph.0001799 |
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author | Palmer, Megan Seddon, James A. van der Zalm, Marieke M. Hesseling, Anneke C. Goussard, Pierre Schaaf, H. Simon Morrison, Julie van Ginneken, Bram Melendez, Jaime Walters, Elisabetta Murphy, Keelin |
author_facet | Palmer, Megan Seddon, James A. van der Zalm, Marieke M. Hesseling, Anneke C. Goussard, Pierre Schaaf, H. Simon Morrison, Julie van Ginneken, Bram Melendez, Jaime Walters, Elisabetta Murphy, Keelin |
author_sort | Palmer, Megan |
collection | PubMed |
description | Diagnostic tools for paediatric tuberculosis remain limited, with heavy reliance on clinical algorithms which include chest x-ray. Computer aided detection (CAD) for tuberculosis on chest x-ray has shown promise in adults. We aimed to measure and optimise the performance of an adult CAD system, CAD4TB, to identify tuberculosis on chest x-rays from children with presumptive tuberculosis. Chest x-rays from 620 children <13 years enrolled in a prospective observational diagnostic study in South Africa, were evaluated. All chest x-rays were read by a panel of expert readers who attributed each with a radiological reference of either ‘tuberculosis’ or ‘not tuberculosis’. Of the 525 chest x-rays included in this analysis, 80 (40 with a reference of ‘tuberculosis’ and 40 with ‘not tuberculosis’) were allocated to an independent test set. The remainder made up the training set. The performance of CAD4TB to identify ‘tuberculosis’ versus ‘not tuberculosis’ on chest x-ray against the radiological reference read was calculated. The CAD4TB software was then fine-tuned using the paediatric training set. We compared the performance of the fine-tuned model to the original model. Our findings were that the area under the receiver operating characteristic curve (AUC) of the original CAD4TB model, prior to fine-tuning, was 0.58. After fine-tuning there was an improvement in the AUC to 0.72 (p = 0.0016). In this first-ever description of the use of CAD to identify tuberculosis on chest x-ray in children, we demonstrate a significant improvement in the performance of CAD4TB after fine-tuning with a set of well-characterised paediatric chest x-rays. CAD has the potential to be a useful additional diagnostic tool for paediatric tuberculosis. We recommend replicating the methods we describe using a larger chest x-ray dataset from a more diverse population and evaluating the potential role of CAD to replace a human-read chest x-ray within treatment-decision algorithms for paediatric tuberculosis. |
format | Online Article Text |
id | pubmed-10187911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101879112023-05-17 Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children Palmer, Megan Seddon, James A. van der Zalm, Marieke M. Hesseling, Anneke C. Goussard, Pierre Schaaf, H. Simon Morrison, Julie van Ginneken, Bram Melendez, Jaime Walters, Elisabetta Murphy, Keelin PLOS Glob Public Health Research Article Diagnostic tools for paediatric tuberculosis remain limited, with heavy reliance on clinical algorithms which include chest x-ray. Computer aided detection (CAD) for tuberculosis on chest x-ray has shown promise in adults. We aimed to measure and optimise the performance of an adult CAD system, CAD4TB, to identify tuberculosis on chest x-rays from children with presumptive tuberculosis. Chest x-rays from 620 children <13 years enrolled in a prospective observational diagnostic study in South Africa, were evaluated. All chest x-rays were read by a panel of expert readers who attributed each with a radiological reference of either ‘tuberculosis’ or ‘not tuberculosis’. Of the 525 chest x-rays included in this analysis, 80 (40 with a reference of ‘tuberculosis’ and 40 with ‘not tuberculosis’) were allocated to an independent test set. The remainder made up the training set. The performance of CAD4TB to identify ‘tuberculosis’ versus ‘not tuberculosis’ on chest x-ray against the radiological reference read was calculated. The CAD4TB software was then fine-tuned using the paediatric training set. We compared the performance of the fine-tuned model to the original model. Our findings were that the area under the receiver operating characteristic curve (AUC) of the original CAD4TB model, prior to fine-tuning, was 0.58. After fine-tuning there was an improvement in the AUC to 0.72 (p = 0.0016). In this first-ever description of the use of CAD to identify tuberculosis on chest x-ray in children, we demonstrate a significant improvement in the performance of CAD4TB after fine-tuning with a set of well-characterised paediatric chest x-rays. CAD has the potential to be a useful additional diagnostic tool for paediatric tuberculosis. We recommend replicating the methods we describe using a larger chest x-ray dataset from a more diverse population and evaluating the potential role of CAD to replace a human-read chest x-ray within treatment-decision algorithms for paediatric tuberculosis. Public Library of Science 2023-05-16 /pmc/articles/PMC10187911/ /pubmed/37192175 http://dx.doi.org/10.1371/journal.pgph.0001799 Text en © 2023 Palmer et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Palmer, Megan Seddon, James A. van der Zalm, Marieke M. Hesseling, Anneke C. Goussard, Pierre Schaaf, H. Simon Morrison, Julie van Ginneken, Bram Melendez, Jaime Walters, Elisabetta Murphy, Keelin Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children |
title | Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children |
title_full | Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children |
title_fullStr | Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children |
title_full_unstemmed | Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children |
title_short | Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children |
title_sort | optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in south african children |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187911/ https://www.ncbi.nlm.nih.gov/pubmed/37192175 http://dx.doi.org/10.1371/journal.pgph.0001799 |
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