Could automated analysis of chest X-rays detect early bronchiectasis in children?

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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00431-021-04061-8.