ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2021
Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- On the interception of the optimal imaging plane for pleural line scanning with automatic robot assisted lung ultrasound: An experimental study.The ultrasound journal · 2026Article
- A Dual-Modality Ultrasound Video Recognition Model for Distinguishing Subpleural Pulmonary Nodules.Mayo Clinic proceedings. Innovations, quality & outcomes · 2025Article
- An 8-point scale lung ultrasound scoring network fusing local detail and global features.Scientific reports · 2025Article
- COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.Biomedical signal processing and control · 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
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- Diagnosis and Prognosis Evaluation of Severe Pneumonia by Lung Ultrasound Score Combined with Serum Inflammatory Markers.Mediterranean journal of hematology and infectious diseases · 2023Article
- State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses.Ultrasound in medicine & biology · 2022Review
- Automatic deep learning-based consolidation/collapse classification in lung ultrasound images for COVID-19 induced pneumonia.Scientific reports · 2022Article
- Review of COVID-19 testing and diagnostic methods.Talanta · 2022Review
- Evaluation of Pulmonary Edema Using Ultrasound Imaging in Patients With COVID-19 Pneumonia Based on a Non-local Channel Attention ResNet.Ultrasound in medicine & biology · 2022Article
- Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model.Biomedical signal processing and control · 2022Article
- Quantitative Analysis of Pleural Line and B-Lines in Lung Ultrasound Images for Severity Assessment of COVID-19 Pneumonia.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022Article
Corrections and comments
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Authors and funding
10 authors.
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Abstract
As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic. By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity. However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians. Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN. A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians. All images were processed via a series of computer-aided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction. A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS. Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation. The model with 128×256 two fully connected layers gave the best accuracy of 87%. It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics.
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