Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.
Department of Physics, University of Western Australia, Perth, Australia.
Eur J Pediatr. 2021 Oct;180(10):3171-3179. doi: 10.1007/s00431-021-04061-8. Epub 2021 Apr 28.
Non-cystic fibrosis bronchiectasis is increasingly described in the paediatric population. While diagnosis is by high-resolution chest computed tomography (CT), chest X-rays (CXRs) remain a first-line investigation. CXRs are currently insensitive in their detection of bronchiectasis. We aim to determine if quantitative digital analysis allows CT features of bronchiectasis to be detected in contemporaneously taken CXRs. Regions of radiologically (A) normal, (B) severe bronchiectasis, (C) mild airway dilation and (D) other parenchymal abnormalities were identified in CT and mapped to corresponding CXR. An artificial neural network (ANN) algorithm was used to characterise regions of classes A, B, C and D. The algorithm was then tested in 13 subjects and compared to CT scan features. Structural changes in CT were reflected in CXR, including mild airway dilation. The areas under the receiver operator curve for ANN feature detection were 0.74 (class A), 0.71 (class B), 0.76 (class C) and 0.86 (class D). CXR analysis identified CT measures of abnormality with a better correlation than standard radiological scoring at the 99% confidence level.Conclusion: Regional abnormalities can be detected by digital analysis of CXR, which may provide a low-cost and readily available tool to indicate the need for diagnostic CT and for ongoing disease monitoring. What is Known: • Bronchiectasis is a severe chronic respiratory disorder increasingly recognised in paediatric populations. • Diagnostic computed tomography imaging is often requested only after several chest X-ray investigations. What is New: • We show that a digital analysis of chest X-ray could provide more accurate identification of bronchiectasis features.
非囊性纤维化性支气管扩张症在儿科人群中越来越常见。虽然高分辨率胸部计算机断层扫描（CT）可用于诊断，但胸部 X 光（CXR）仍然是一线检查。目前，CXR 在检测支气管扩张症方面不够敏感。我们旨在确定定量数字分析是否允许在同时进行的 CXR 中检测到支气管扩张症的 CT 特征。在 CT 中确定了放射学上（A）正常、（B）严重支气管扩张、（C）轻度气道扩张和（D）其他实质异常的区域，并将其映射到相应的 CXR 上。使用人工神经网络（ANN）算法来描述 A、B、C 和 D 类区域的特征。然后，该算法在 13 名受试者中进行了测试，并与 CT 扫描特征进行了比较。CT 中的结构变化反映在 CXR 中，包括轻度气道扩张。ANN 特征检测的受试者工作特征曲线下面积分别为 0.74（A 类）、0.71（B 类）、0.76（C 类）和 0.86（D 类）。CXR 分析在 99%置信水平下比标准放射评分更能准确地检测到 CT 异常指标。结论：通过 CXR 的数字分析可以检测到区域性异常，这可能提供一种低成本且易于获得的工具，以指示需要进行诊断性 CT 检查和进行疾病监测。已知：·支气管扩张症是一种严重的慢性呼吸系统疾病，在儿科人群中越来越常见。·诊断性计算机断层成像通常仅在多次胸部 X 光检查后才会要求进行。新内容：·我们表明，胸部 X 光的数字分析可以更准确地识别支气管扩张症特征。
