Performance of Computer‐Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China
ABSTRACT Background Computer‐aided detection (CAD) software has been introduced to automatically interpret digital chest X‐rays. This study aimed to evaluate the performance of CAD software (JF CXR‐1 v3.0, which was developed by a domestic Hi‐tech enterprise) in tuberculosis (TB) case finding in Chi...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Chronic Diseases and Translational Medicine |
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| Online Access: | https://doi.org/10.1002/cdt3.70001 |
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| author | Xuefang Cao Boxuan Feng Bin Zhang Dakuan Wang Jiang Du Yijun He Tonglei Guo Shouguo Pan Zisen Liu Jiaoxia Yan Qi Jin Lei Gao Henan Xin |
| author_facet | Xuefang Cao Boxuan Feng Bin Zhang Dakuan Wang Jiang Du Yijun He Tonglei Guo Shouguo Pan Zisen Liu Jiaoxia Yan Qi Jin Lei Gao Henan Xin |
| author_sort | Xuefang Cao |
| collection | DOAJ |
| description | ABSTRACT Background Computer‐aided detection (CAD) software has been introduced to automatically interpret digital chest X‐rays. This study aimed to evaluate the performance of CAD software (JF CXR‐1 v3.0, which was developed by a domestic Hi‐tech enterprise) in tuberculosis (TB) case finding in China. Methods In 2019, we conducted an internal evaluation of the performance of JF CXR‐1 v3.0 by reading standard images annotated by a panel of experts. In 2020, using the reading results of chest X‐rays by a panel of experts as the reference standard, we conducted an on‐site prospective study to evaluate the performance of JF CXR‐1 v3.0 and local radiologists in TB case finding in 13 township health centers in Zhongmu County, Henan Province. Results Internal assessment results based on 277 standard images showed that JF CXR‐1 v3.0 had a sensitivity of 85.94% (95% confidence interval [CI]: 77.42%, 94.45%) and a specificity of 74.65% (95% CI: 68.81%, 80.49%) to distinguish active TB from other imaging conditions. In the on‐site evaluation phase, images from 3705 outpatients who underwent chest X‐ray detection were read by JF CXR‐1 v3.0 and local radiologists in parallel. The imaging diagnosis of local radiologists for active TB had a sensitivity of 32.89% (95% CI: 22.33%, 43.46%) and a specificity of 99.28% (95% CI: 99.01%, 99.56%), while JF CXR‐1 v3.0 showed a significantly higher sensitivity of 92.11% (95% CI: 86.04%, 98.17%) (p < 0.05) and maintained high specificity at 94.54% (95% CI: 93.81%, 95.28%). Conclusions CAD software could play a positive role in improving the TB case finding capability of township health centers. |
| format | Article |
| id | doaj-art-ec4e3f60ca834e9a976e890ad11039ba |
| institution | OA Journals |
| issn | 2589-0514 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Chronic Diseases and Translational Medicine |
| spelling | doaj-art-ec4e3f60ca834e9a976e890ad11039ba2025-08-20T02:03:35ZengWileyChronic Diseases and Translational Medicine2589-05142025-06-0111214014710.1002/cdt3.70001Performance of Computer‐Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in ChinaXuefang Cao0Boxuan Feng1Bin Zhang2Dakuan Wang3Jiang Du4Yijun He5Tonglei Guo6Shouguo Pan7Zisen Liu8Jiaoxia Yan9Qi Jin10Lei Gao11Henan Xin12NHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, and Center for Tuberculosis Research Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaNHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, and Center for Tuberculosis Research Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaCenter for Disease Control and Prevention of Zhongmu County Zhengzhou Henan ChinaCenter for Disease Control and Prevention of Zhongmu County Zhengzhou Henan ChinaNHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, and Center for Tuberculosis Research Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaNHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, and Center for Tuberculosis Research Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaNHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, and Center for Tuberculosis Research Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaCenter for Disease Control and Prevention of Zhongmu County Zhengzhou Henan ChinaCenter for Disease Control and Prevention of Zhongmu County Zhengzhou Henan ChinaCenter for Disease Control and Prevention of Zhongmu County Zhengzhou Henan ChinaNHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, and Center for Tuberculosis Research Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaNHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, and Center for Tuberculosis Research Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaNHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, and Center for Tuberculosis Research Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaABSTRACT Background Computer‐aided detection (CAD) software has been introduced to automatically interpret digital chest X‐rays. This study aimed to evaluate the performance of CAD software (JF CXR‐1 v3.0, which was developed by a domestic Hi‐tech enterprise) in tuberculosis (TB) case finding in China. Methods In 2019, we conducted an internal evaluation of the performance of JF CXR‐1 v3.0 by reading standard images annotated by a panel of experts. In 2020, using the reading results of chest X‐rays by a panel of experts as the reference standard, we conducted an on‐site prospective study to evaluate the performance of JF CXR‐1 v3.0 and local radiologists in TB case finding in 13 township health centers in Zhongmu County, Henan Province. Results Internal assessment results based on 277 standard images showed that JF CXR‐1 v3.0 had a sensitivity of 85.94% (95% confidence interval [CI]: 77.42%, 94.45%) and a specificity of 74.65% (95% CI: 68.81%, 80.49%) to distinguish active TB from other imaging conditions. In the on‐site evaluation phase, images from 3705 outpatients who underwent chest X‐ray detection were read by JF CXR‐1 v3.0 and local radiologists in parallel. The imaging diagnosis of local radiologists for active TB had a sensitivity of 32.89% (95% CI: 22.33%, 43.46%) and a specificity of 99.28% (95% CI: 99.01%, 99.56%), while JF CXR‐1 v3.0 showed a significantly higher sensitivity of 92.11% (95% CI: 86.04%, 98.17%) (p < 0.05) and maintained high specificity at 94.54% (95% CI: 93.81%, 95.28%). Conclusions CAD software could play a positive role in improving the TB case finding capability of township health centers.https://doi.org/10.1002/cdt3.70001artificial intelligencecase findingchest X‐raycomputer‐aided detectiontuberculosis |
| spellingShingle | Xuefang Cao Boxuan Feng Bin Zhang Dakuan Wang Jiang Du Yijun He Tonglei Guo Shouguo Pan Zisen Liu Jiaoxia Yan Qi Jin Lei Gao Henan Xin Performance of Computer‐Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China Chronic Diseases and Translational Medicine artificial intelligence case finding chest X‐ray computer‐aided detection tuberculosis |
| title | Performance of Computer‐Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China |
| title_full | Performance of Computer‐Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China |
| title_fullStr | Performance of Computer‐Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China |
| title_full_unstemmed | Performance of Computer‐Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China |
| title_short | Performance of Computer‐Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China |
| title_sort | performance of computer aided detection software in tuberculosis case finding in township health centers in china |
| topic | artificial intelligence case finding chest X‐ray computer‐aided detection tuberculosis |
| url | https://doi.org/10.1002/cdt3.70001 |
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