A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm

Yang，Yang1; Xia，Lu2; Liu，Ping1; Yang，Fuping3; Wu，Yuqing4; Pan，Hongqiu5; Hou，Dailun6; Liu，Ning7; Lu，Shuihua2

2023

10.3389/fmed.2023.1195451

Frontiers in Medicine   影响因子和分区

2296-858X

Background: Chest radiography (chest X-ray or CXR) plays an important role in the early detection of active pulmonary tuberculosis (TB). In areas with a high TB burden that require urgent screening, there is often a shortage of radiologists available to interpret the X-ray results. Computer-aided detection (CAD) software employed with artificial intelligence (AI) systems may have the potential to solve this problem. Objective: We validated the effectiveness and safety of pulmonary tuberculosis imaging screening software that is based on a convolutional neural network algorithm. Methods: We conducted prospective multicenter clinical research to validate the performance of pulmonary tuberculosis imaging screening software (JF CXR-1). Volunteers under the age of 15 years, both with or without suspicion of pulmonary tuberculosis, were recruited for CXR photography. The software reported a probability score of TB for each participant. The results were compared with those reported by radiologists. We measured sensitivity, specificity, consistency rate, and the area under the receiver operating characteristic curves (AUC) for the diagnosis of tuberculosis. Besides, adverse events (AE) and severe adverse events (SAE) were also evaluated. Results: The clinical research was conducted in six general infectious disease hospitals across China. A total of 1,165 participants were enrolled, and 1,161 were enrolled in the full analysis set (FAS). Men accounted for 60.0% (697/1,161). Compared to the results from radiologists on the board, the software showed a sensitivity of 94.2% (95% CI: 92.0–95.8%) and a specificity of 91.2% (95% CI: 88.5–93.2%). The consistency rate was 92.7% (91.1–94.1%), with a Kappa value of 0.854 (P = 0.000). The AUC was 0.98. In the safety set (SS), which consisted of 1,161 participants, 0.3% (3/1,161) had AEs that were not related to the software, and no severe AEs were observed. Conclusion: The software for tuberculosis screening based on a convolutional neural network algorithm is effective and safe. It is a potential candidate for solving tuberculosis screening problems in areas lacking radiologists with a high TB burden.

computer-aided detection (CAD) convolutional neural network algorithm deep learning diagnosis radiography screening tuberculosis

SCI

英语

通讯

WOS:001057140900001

2-s2.0-85169336966

Scopus

1.Department of Tuberculosis,Shanghai Public Health Clinical Center Affiliated to Fudan University,Shanghai,China
2.Department of Pulmonary Medicine,National Clinical Research Center for Infectious Disease,Shenzhen Third People's Hospital,The Second Affiliated Hospital,School of Medicine,Southern University of Science and Technology,Shenzhen,Guangdong,China
3.Department of Tuberculosis,Chongqing Public Health Medical Center,Southwest University,Chongqing,China
4.Department of Tuberculosis,Jiangxi Chest Hospital,Nanchang,Jiangxi,China
5.Department of Tuberculosis,The Third Hospital of Zhenjiang,Zhenjiang,Jiangsu,China
6.Department of Radiology,Beijing Chest Hospital,Capital Medical University,Beijing,China
7.Department of Tuberculosis,Hebei Chest Hospital,Shijiangzhuang,Hebei,China

Yang，Yang,Xia，Lu,Liu，Ping,et al. A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm[J]. Frontiers in Medicine,2023,10.

Yang，Yang.,Xia，Lu.,Liu，Ping.,Yang，Fuping.,Wu，Yuqing.,...&Lu，Shuihua.(2023).A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm.Frontiers in Medicine,10.

Yang，Yang,et al."A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm".Frontiers in Medicine 10(2023).