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 < 0.001. Among features describing p-line margin morphology, projected intensity deviation showed the largest difference between COVID-19 cases (4.08 ± 0.32) and normal (0.43 ± 0.06), P < 0.001. From the TLT line features, only 2 features, gray-level non-uniformity and run-length non-uniformity, showed a significant difference between normal cases (0.32 ± 0.06, 0.59 ± 0.06) and COVID-19 (0.22 ± 0.02, 0.39 ± 0.05), P = 0.04, respectively. All features together for p-line showed perfect sensitivity and specificity of 100; whereas, TLT features had a sensitivity of 90 and specificity of 70. Observer agreement for p-lines (ICC = 0.65-0.85) was higher than for TLT features (ICC = 0.42-0.72). CONCLUSION: P-line features characterize COVID-19 changes with high accuracy and outperform TLT features. Quantitative p-line features are promising diagnostic tools in the interpretation of lung ultrasound images in the context of COVID-19.