Fehr Jana, Konigorski Stefan, Olivier Stephen, Gunda Resign, Surujdeen Ashmika, Gareta Dickman, Smit Theresa, Baisley Kathy, Moodley Sashen, Moosa Yumna, Hanekom Willem, Koole Olivier, Ndung'u Thumbi, Pillay Deenan, Grant Alison D, Siedner Mark J, Lippert Christoph, Wong Emily B
Africa Health Research Institute, KwaZulu-Natal, South Africa.
Digital Health & Machine Learning, Hasso Plattner Institute for Digital Engineering, Berlin, Germany.
NPJ Digit Med. 2021 Jul 2;4(1):106. doi: 10.1038/s41746-021-00471-y.
Computer-aided digital chest radiograph interpretation (CAD) can facilitate high-throughput screening for tuberculosis (TB), but its use in population-based active case-finding programs has been limited. In an HIV-endemic area in rural South Africa, we used a CAD algorithm (CAD4TBv5) to interpret digital chest x-rays (CXR) as part of a mobile health screening effort. Participants with TB symptoms or CAD4TBv5 score above the triaging threshold were referred for microbiological sputum assessment. During an initial pilot phase, a low CAD4TBv5 triaging threshold of 25 was selected to maximize TB case finding. We report the performance of CAD4TBv5 in screening 9,914 participants, 99 (1.0%) of whom were found to have microbiologically proven TB. CAD4TBv5 was able to identify TB cases at the same sensitivity but lower specificity as a blinded radiologist, whereas the next generation of the algorithm (CAD4TBv6) achieved comparable sensitivity and specificity to the radiologist. The CXRs of people with microbiologically confirmed TB spanned a range of lung field abnormality, including 19 (19.2%) cases deemed normal by the radiologist. HIV serostatus did not impact CAD4TB's performance. Notably, 78.8% of the TB cases identified during this population-based survey were asymptomatic and therefore triaged for sputum collection on the basis of CAD4TBv5 score alone. While CAD4TBv6 has the potential to replace radiologists for triaging CXRs in TB prevalence surveys, population-specific piloting is necessary to set the appropriate triaging thresholds. Further work on image analysis strategies is needed to identify radiologically subtle active TB.
计算机辅助数字化胸部X光片解读（CAD）有助于结核病（TB）的高通量筛查，但其在基于人群的主动病例发现项目中的应用一直有限。在南非农村的一个艾滋病毒流行地区，我们使用一种CAD算法（CAD4TBv5）来解读数字化胸部X光片（CXR），作为移动健康筛查工作的一部分。有结核病症状或CAD4TBv5评分高于分诊阈值的参与者被转诊进行痰微生物学评估。在最初的试点阶段，选择了25的低CAD4TBv5分诊阈值以最大化结核病病例发现。我们报告了CAD4TBv5在筛查9914名参与者中的表现，其中99人（1.0%）经微生物学证实患有结核病。CAD4TBv5能够以与一名盲法放射科医生相同的敏感性但较低的特异性识别结核病病例，而该算法的下一代（CAD4TBv6）实现了与放射科医生相当的敏感性和特异性。微生物学确诊为结核病的人的CXR显示出一系列肺野异常，包括19例（19.2%）被放射科医生判定为正常的病例。艾滋病毒血清学状态不影响CAD4TB的表现。值得注意的是，在这项基于人群的调查中发现的78.8%的结核病病例无症状，因此仅根据CAD4TBv5评分进行分诊以采集痰液。虽然CAD4TBv6有潜力在结核病患病率调查中取代放射科医生对CXR进行分诊，但需要针对特定人群进行试点以设定合适的分诊阈值。需要进一步开展图像分析策略的研究以识别放射学上细微的活动性结核病。
