ArticleThe Journal of the Acoustical Society of America2021
Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images.
Article in The Journal of the Acoustical Society of America, 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.
- 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
- Practical Applications of Lung and Diaphragm Ultrasound in the Intensive Care Unit: An Updated Narrative Review.Cureus · 2025Review
- 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
- Automatic deep learning-based pleural effusion segmentation in lung ultrasound images.BMC medical informatics and decision making · 2023Article
- Automated Lung Ultrasound Pulmonary Disease Quantification Using an Unsupervised Machine Learning Technique for COVID-19.Diagnostics (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
- State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses.Ultrasound in medicine & biology · 2022Review
- Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic.Journal of imaging · 2022Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Ultrasound in point-of-care lung assessment is becoming increasingly relevant. This is further reinforced in the context of the COVID-19 pandemic, where rapid decisions on the lung state must be made for staging and monitoring purposes. The lung structural changes due to severe COVID-19 modify the way ultrasound propagates in the parenchyma. This is reflected by changes in the appearance of the lung ultrasound images. In abnormal lungs, vertical artifacts known as B-lines appear and can evolve into white lung patterns in the more severe cases. Currently, these artifacts are assessed by trained physicians, and the diagnosis is qualitative and operator dependent. In this article, an automatic segmentation method using a convolutional neural network is proposed to automatically stage the progression of the disease. 1863 B-mode images from 203 videos obtained from 14 asymptomatic individual,14 confirmed COVID-19 cases, and 4 suspected COVID-19 cases were used. Signs of lung damage, such as the presence and extent of B-lines and white lung areas, are manually segmented and scored from zero to three (most severe). These manually scored images are considered as ground truth. Different test-training strategies are evaluated in this study. The results shed light on the efficient approaches and common challenges associated with automatic segmentation methods.
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