Quantitative pleural line characterization outperforms traditional lung texture ultrasound features in detection of COVID‐19

Abstract Background and objective Lung ultrasound is an inherently user‐dependent modality that could benefit from quantitative image analysis. In this pilot study we evaluate the use of computer‐based pleural line (p‐line) ultrasound features in comparison to traditional lung texture (TLT) features to test the hypothesis that p‐line thickening and irregularity are highly suggestive of coronavirus disease 2019 (COVID‐19) and can be used to improve the disease diagnosis on lung ultrasound. Methods Twenty lung ultrasound images, including normal and COVID‐19 cases, were used for quantitative analysis. P‐lines were detected by a semiautomated segmentation method. Seven quantitative features describing thickness, margin morphology, and echo intensity were extracted. TLT lines were outlined, and texture features based on run‐length and gray‐level co‐occurrence matrix were extracted. The diagnostic performance of the 2 feature sets was measured and compared using receiver operating characteristics curve analysis. Observer agreements were evaluated by measuring interclass correlation coefficients (ICC) for each feature. Results Six of 7 p‐line features showed a significant difference between normal and COVID‐19 cases. Thickness of p‐lines was larger in COVID‐19 cases (6.27 ± 1.45 mm) compared to normal (1.00 ± 0.19 mm), P