Yang Yang, Xia Lu, Liu Ping, Yang Fuping, Wu Yuqing, Pan Hongqiu, Hou Dailun, Liu Ning, Lu Shuihua
Department of Tuberculosis, Shanghai Public Health Clinical Center Affiliated to Fudan University, Shanghai, China.
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.
Front Med (Lausanne). 2023 Aug 15;10:1195451. doi: 10.3389/fmed.2023.1195451. eCollection 2023.
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.
We validated the effectiveness and safety of pulmonary tuberculosis imaging screening software that is based on a convolutional neural network algorithm.
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.
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 ( = 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.
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.
胸部X线摄影（胸部X光或CXR）在活动性肺结核（TB）的早期检测中起着重要作用。在结核病负担较高且需要紧急筛查的地区，往往缺乏放射科医生来解读X光检查结果。与人工智能（AI）系统结合使用的计算机辅助检测（CAD）软件可能有潜力解决这一问题。
我们验证了基于卷积神经网络算法的肺结核影像筛查软件的有效性和安全性。
我们开展了前瞻性多中心临床研究，以验证肺结核影像筛查软件（JF CXR-1）的性能。招募了15岁以下、无论是否怀疑患有肺结核的志愿者进行胸部X光摄影。该软件为每位参与者报告结核病的概率评分。将结果与放射科医生报告的结果进行比较。我们测量了诊断结核病的灵敏度、特异度、符合率以及受试者操作特征曲线下面积（AUC）。此外，还评估了不良事件（AE）和严重不良事件（SAE）。
临床研究在中国的6家综合传染病医院进行。共招募了1165名参与者，1161名被纳入全分析集（FAS）。男性占60.0%（697/1161）。与现场放射科医生的结果相比，该软件的灵敏度为94.2%（95%CI：92.0-95.8%），特异度为91.2%（95%CI：88.5-93.2%）。符合率为92.7%（91.1-94.1%），Kappa值为0.854（P = 0.000）。AUC为0.98。在由1161名参与者组成的安全性集（SS）中，0.3%（3/1161）出现了与软件无关的AE，未观察到严重AE。
基于卷积神经网络算法的结核病筛查软件有效且安全。它是解决结核病负担高且缺乏放射科医生地区结核病筛查问题的潜在候选方案。
