Performance of Computer-Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China
- VernacularTitle:Performance of Computer-Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China
- Author:
Xuefang CAO
1
;
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 Information
- Publication Type:Journal Article
- Keywords: artificial intelligence; case finding; chest X-ray; computer-aided detection; tuberculosis
- From: Chronic Diseases and Translational Medicine 2025;11(2):140-147
- CountryChina
- Language:English
- 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.
