PDF(Pubmed)
Abstract：
BACKGROUND: Chest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non--tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature.
METHODS: In this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma.
CONCLUSIONS: Solely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.
METHODS: In this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma.
CONCLUSIONS: Solely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.
摘要：
背景：胸部X线作为结核病筛查工具具有很高的敏感性和可接受的特异性，但是在结核病负担较高的地区，通常缺乏解释胸部X光的放射学专业知识.因此，基于人工智能的计算机辅助检测系统越来越多地用于在数字胸部X光检查中筛查与结核病相关的异常。CAD4TB软件先前已被证明对胸部X线结核相关异常具有高度敏感性，但它尚未校准非结核病异常的检测。在筛查结核病时，计算机辅助检测的用户需要意识到，其他胸部病变可能会像，或者比，活动性肺结核。然而,在撒哈拉以南非洲地区，在胸部X线筛查结核病期间发现的非结核胸部X线异常特征仍然很差，只有很少的文学。
方法：在本例系列中，我们报告了4例在结核病试验+准确性中在CXR上检测到的非结核病异常（ClinicalTrials.gov标识符：NCT04666311），一项针对莱索托和南非卫生机构的成人推定结核病病例的研究，以确定两种潜在结核病分诊测试的诊断准确性：计算机辅助检测(CAD4TBv7，代尔夫特，荷兰)和C反应蛋白(AlereAfinion，美国）。四名黑人非洲参与者表现出以下胸部X射线异常：一名59岁的女性患有肺动静脉畸形，一个28岁的气胸患者,一个患有大面积支气管扩张的20岁男子,还有一个47岁的女人患有曲霉菌.
结论：在撒哈拉以南非洲，仅使用基于人工智能的胸部X射线计算机辅助检测系统作为结核病筛查策略是有好处的，但也有风险。由于CAD4TB用于非结核异常识别的局限性,计算机辅助检测软件可能会错过需要治疗的重大胸部X光检查异常，如我们的四个案例所示。增加数据收集，需要描述非结核病异常的特征，并研究这些疾病对撒哈拉以南非洲的个人和卫生系统的影响，以帮助改善现有的人工智能软件程序及其在结核病负担高的国家的使用。
方法：在本例系列中，我们报告了4例在结核病试验+准确性中在CXR上检测到的非结核病异常（ClinicalTrials.gov标识符：NCT04666311），一项针对莱索托和南非卫生机构的成人推定结核病病例的研究，以确定两种潜在结核病分诊测试的诊断准确性：计算机辅助检测(CAD4TBv7，代尔夫特，荷兰)和C反应蛋白(AlereAfinion，美国）。四名黑人非洲参与者表现出以下胸部X射线异常：一名59岁的女性患有肺动静脉畸形，一个28岁的气胸患者,一个患有大面积支气管扩张的20岁男子,还有一个47岁的女人患有曲霉菌.
结论：在撒哈拉以南非洲，仅使用基于人工智能的胸部X射线计算机辅助检测系统作为结核病筛查策略是有好处的，但也有风险。由于CAD4TB用于非结核异常识别的局限性,计算机辅助检测软件可能会错过需要治疗的重大胸部X光检查异常，如我们的四个案例所示。增加数据收集，需要描述非结核病异常的特征，并研究这些疾病对撒哈拉以南非洲的个人和卫生系统的影响，以帮助改善现有的人工智能软件程序及其在结核病负担高的国家的使用。
