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

Abstract

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.

Keywords: Bronchiectasis; Chest X-rays; Children; Computed tomography; Image analysis.

Fig. 1

Methodological steps for identifying regions of CXR for classification. a Abnormal regions are identified on CT and b mapped to the posteroanterior projection. c The lung shape is segmented from CXR images and d split into a grid of evenly sized squares. e Each square is classified by comparison to the posteroanterior CT projection, and classification of normal and abnormal regions conducted by an artificial neural network analysis

Fig. 2

Receiver operating characteristic (ROC) for detection of each class of tissue in CXR. The areas under the ROC curves are 0.74 (A—normal tissue), 0.71 (B—definitive bronchiectasis), 0.76 (C—airway dilation alone) and 0.86 (D—parenchymal abnormalities). True positives are blocks of tissue correctly classified by the algorithm and false positives are those identified in any incorrect class (e.g. a definitive bronchiectasis region identified as normal, airway dilation or parenchymal abnormalities)

Fig. 3

a, c, e Correlations between CT features of bronchiectasis and the number of pixel blocks detected by our automated algorithm as abnormal in 13 unseen subjects, and b, d, f the same CT features correlated with Brasfield scores in the same 13 subjects. In each case, the correlation is more significant using the automated analysis, than the semi-quantitative radiological assessment