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