The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study
From 2020 to 2022, a pulmonary tuberculosis (PTB) active case finding project based on chest X-ray (CXR) examination was conducted targeting individuals aged ≥65 years old in Jiangshan County, Quzhou City. The current study used computer-aided detection (CAD) software (JF CXR-1 v2) to retrospectivel...
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Taylor & Francis Group
2025-12-01
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| Series: | Emerging Microbes and Infections |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22221751.2025.2470998 |
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| author | Henan Xin Wei Wang Xiaomeng Wang Juanjuan Huang Yuanzhi Di Jiang Du Xuefang Cao Boxuan Feng Lingyu Shen Yijun He Tonglei Guo Zihan Li Jianguo Liang Zhen Wang Ping Zhu Lei Gao |
| author_facet | Henan Xin Wei Wang Xiaomeng Wang Juanjuan Huang Yuanzhi Di Jiang Du Xuefang Cao Boxuan Feng Lingyu Shen Yijun He Tonglei Guo Zihan Li Jianguo Liang Zhen Wang Ping Zhu Lei Gao |
| author_sort | Henan Xin |
| collection | DOAJ |
| description | From 2020 to 2022, a pulmonary tuberculosis (PTB) active case finding project based on chest X-ray (CXR) examination was conducted targeting individuals aged ≥65 years old in Jiangshan County, Quzhou City. The current study used computer-aided detection (CAD) software (JF CXR-1 v2) to retrospectively analyze the CXR images and to estimate its potential capacity for identifying PTB cases. The information of notified microbiologically confirmed PTB among the participants were exported from the Tuberculosis Information Management System. A total of 49,919 subjects participated in the 2020 examinations. Of these, 40,741 and 39,185 completed the follow-up surveys in 2021 and 2022, respectively. The pooled prevalence of suspected PTB reported by radiologists was 1.21% (1579/129,776), compared with 12.43% (16,129/129,776) reported by CAD. Of 101 bacteriologically confirmed PTB cases notified over three years, radiologists and CAD reported 45.54% (46/101) and 83.16% (84/101) as suspected cases, respectively. Among subjects with abnormal CAD (CAD score>0.35), the majority of the notified confirmed PTB patients (63/84) had their CAD scores >75% quantiles (as>0.75). With 3 years’ results, their CAD scores exhibited dynamic changes along with disease progression or treatment with median scores peaking in the year of diagnosis. This intriguing finding suggests that CAD for CXR reading assisted radiologists in PTB screening by reducing workload and improving case finding. The CAD primary score may have the potential to identify high-risk individuals and early PTB patients, adding a new dimension to our understanding of disease progression. |
| format | Article |
| id | doaj-art-e23aa026e6ab415c96997cd92a197326 |
| institution | Kabale University |
| issn | 2222-1751 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Emerging Microbes and Infections |
| spelling | doaj-art-e23aa026e6ab415c96997cd92a1973262025-08-20T03:53:23ZengTaylor & Francis GroupEmerging Microbes and Infections2222-17512025-12-0114110.1080/22221751.2025.2470998The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort studyHenan Xin0Wei Wang1Xiaomeng Wang2Juanjuan Huang3Yuanzhi Di4Jiang Du5Xuefang Cao6Boxuan Feng7Lingyu Shen8Yijun He9Tonglei Guo10Zihan Li11Jianguo Liang12Zhen Wang13Ping Zhu14Lei Gao15NHC 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, People’s Republic of ChinaQuzhou City Center for Diseases Control and Prevention, People’s Republic of ChinaZhejiang Provincial Center for Diseases Control and Prevention, People’s Republic of 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, People’s Republic of 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, People’s Republic of 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, People’s Republic of 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, People’s Republic of 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, People’s Republic of 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, People’s Republic of 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, People’s Republic of 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, People’s Republic of 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, People’s Republic of 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, People’s Republic of ChinaZhejiang Provincial Center for Diseases Control and Prevention, People’s Republic of ChinaQuzhou City Center for Diseases Control and Prevention, People’s Republic of 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, People’s Republic of ChinaFrom 2020 to 2022, a pulmonary tuberculosis (PTB) active case finding project based on chest X-ray (CXR) examination was conducted targeting individuals aged ≥65 years old in Jiangshan County, Quzhou City. The current study used computer-aided detection (CAD) software (JF CXR-1 v2) to retrospectively analyze the CXR images and to estimate its potential capacity for identifying PTB cases. The information of notified microbiologically confirmed PTB among the participants were exported from the Tuberculosis Information Management System. A total of 49,919 subjects participated in the 2020 examinations. Of these, 40,741 and 39,185 completed the follow-up surveys in 2021 and 2022, respectively. The pooled prevalence of suspected PTB reported by radiologists was 1.21% (1579/129,776), compared with 12.43% (16,129/129,776) reported by CAD. Of 101 bacteriologically confirmed PTB cases notified over three years, radiologists and CAD reported 45.54% (46/101) and 83.16% (84/101) as suspected cases, respectively. Among subjects with abnormal CAD (CAD score>0.35), the majority of the notified confirmed PTB patients (63/84) had their CAD scores >75% quantiles (as>0.75). With 3 years’ results, their CAD scores exhibited dynamic changes along with disease progression or treatment with median scores peaking in the year of diagnosis. This intriguing finding suggests that CAD for CXR reading assisted radiologists in PTB screening by reducing workload and improving case finding. The CAD primary score may have the potential to identify high-risk individuals and early PTB patients, adding a new dimension to our understanding of disease progression.https://www.tandfonline.com/doi/10.1080/22221751.2025.2470998Pulmonary tuberculosisscreeningcomputer-aided detectionchest radiographyearly finding |
| spellingShingle | Henan Xin Wei Wang Xiaomeng Wang Juanjuan Huang Yuanzhi Di Jiang Du Xuefang Cao Boxuan Feng Lingyu Shen Yijun He Tonglei Guo Zihan Li Jianguo Liang Zhen Wang Ping Zhu Lei Gao The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study Emerging Microbes and Infections Pulmonary tuberculosis screening computer-aided detection chest radiography early finding |
| title | The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study |
| title_full | The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study |
| title_fullStr | The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study |
| title_full_unstemmed | The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study |
| title_short | The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study |
| title_sort | performance of computer aided detection for chest radiography in tuberculosis screening a population based retrospective cohort study |
| topic | Pulmonary tuberculosis screening computer-aided detection chest radiography early finding |
| url | https://www.tandfonline.com/doi/10.1080/22221751.2025.2470998 |
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