ArticleJournal of the American College of Emergency Physicians open2021
Quantitative pleural line characterization outperforms traditional lung texture ultrasound features in detection of COVID-19.
Article in Journal of the American College of Emergency Physicians open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- ChatGPT-4-Driven Liver Ultrasound Radiomics Analysis: Diagnostic Value and Drawbacks in a Comparative Study.JMIR AI · 2025Article
- Prognostication and integration of bedside lung ultrasound and computed tomography imaging findings with clinical features to Predict COVID-19 In-hospital mortality and ICU admission.Emergency radiology · 2025Article
- Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound.AI (Basel, Switzerland) · 2023Article
- New International Guidelines and Consensus on the Use of Lung Ultrasound.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2023Review
- Ultrasound Radiomics for the Detection of Early-Stage Liver Fibrosis.Diagnostics (Basel, Switzerland) · 2022Article
- Can Sequential Images from the Same Object Be Used for Training Machine Learning Models? A Case Study for Detecting Liver Disease by Ultrasound Radiomics.AI (Basel, Switzerland) · 2022Article
- Pleural abnormalities in COVID-19: a narrative review.Journal of thoracic disease · 2021Review
- Quantitative pleural line characterization outperforms traditional lung texture ultrasound features in detection of COVID-19.Journal of the American College of Emergency Physicians open · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveLung 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.
methodsTwenty 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.
resultsSix 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),
conclusionP-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.
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