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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

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., Competing Interests: The authors have declared no conflict of interest., (© 2021 The Authors. JACEP Open published by Wiley Periodicals LLC on behalf of American College of Emergency Physicians.)