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Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis
There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with differen...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668935/ https://www.ncbi.nlm.nih.gov/pubmed/34903808 http://dx.doi.org/10.1038/s41598-021-03265-0 |
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author | Codlin, Andrew J. Dao, Thang Phuoc Vo, Luan Nguyen Quang Forse, Rachel J. Van Truong, Vinh Dang, Ha Minh Nguyen, Lan Huu Nguyen, Hoa Binh Nguyen, Nhung Viet Sidney-Annerstedt, Kristi Squire, Bertie Lönnroth, Knut Caws, Maxine |
author_facet | Codlin, Andrew J. Dao, Thang Phuoc Vo, Luan Nguyen Quang Forse, Rachel J. Van Truong, Vinh Dang, Ha Minh Nguyen, Lan Huu Nguyen, Hoa Binh Nguyen, Nhung Viet Sidney-Annerstedt, Kristi Squire, Bertie Lönnroth, Knut Caws, Maxine |
author_sort | Codlin, Andrew J. |
collection | PubMed |
description | There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with different levels of experience and then processed by 12 CAD software solutions. Using Xpert MTB/RIF results as the reference standard, we compared the performance characteristics of each CAD software against both an Expert and Intermediate Reader, using cut-off thresholds which were selected to match the sensitivity of each human reader. Six CAD systems performed on par with the Expert Reader (Qure.ai, DeepTek, Delft Imaging, JF Healthcare, OXIPIT, and Lunit) and one additional software (Infervision) performed on par with the Intermediate Reader only. Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the Intermediate Reader. The majority of these CAD software showed significantly lower performance among participants with a past history of TB. The radiography equipment used to capture the CXR image was also shown to affect performance for some CAD software. TB program implementers now have a wide selection of quality CAD software solutions to utilize in their CXR screening initiatives. |
format | Online Article Text |
id | pubmed-8668935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-86689352021-12-15 Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis Codlin, Andrew J. Dao, Thang Phuoc Vo, Luan Nguyen Quang Forse, Rachel J. Van Truong, Vinh Dang, Ha Minh Nguyen, Lan Huu Nguyen, Hoa Binh Nguyen, Nhung Viet Sidney-Annerstedt, Kristi Squire, Bertie Lönnroth, Knut Caws, Maxine Sci Rep Article There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with different levels of experience and then processed by 12 CAD software solutions. Using Xpert MTB/RIF results as the reference standard, we compared the performance characteristics of each CAD software against both an Expert and Intermediate Reader, using cut-off thresholds which were selected to match the sensitivity of each human reader. Six CAD systems performed on par with the Expert Reader (Qure.ai, DeepTek, Delft Imaging, JF Healthcare, OXIPIT, and Lunit) and one additional software (Infervision) performed on par with the Intermediate Reader only. Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the Intermediate Reader. The majority of these CAD software showed significantly lower performance among participants with a past history of TB. The radiography equipment used to capture the CXR image was also shown to affect performance for some CAD software. TB program implementers now have a wide selection of quality CAD software solutions to utilize in their CXR screening initiatives. Nature Publishing Group UK 2021-12-13 /pmc/articles/PMC8668935/ /pubmed/34903808 http://dx.doi.org/10.1038/s41598-021-03265-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Codlin, Andrew J. Dao, Thang Phuoc Vo, Luan Nguyen Quang Forse, Rachel J. Van Truong, Vinh Dang, Ha Minh Nguyen, Lan Huu Nguyen, Hoa Binh Nguyen, Nhung Viet Sidney-Annerstedt, Kristi Squire, Bertie Lönnroth, Knut Caws, Maxine Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis |
title | Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis |
title_full | Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis |
title_fullStr | Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis |
title_full_unstemmed | Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis |
title_short | Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis |
title_sort | independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668935/ https://www.ncbi.nlm.nih.gov/pubmed/34903808 http://dx.doi.org/10.1038/s41598-021-03265-0 |
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