ArticleIEEE journal of biomedical and health informatics2023
Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.
Article in IEEE journal of biomedical and health informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for lung ultrasound interpretation: a systematic review.Frontiers in radiology · 2026Pooled it
- Multimodal Diagnostic Ultrasound for Congestion, Perfusion, and Ultrafiltration Tolerance in Maintenance Hemodialysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Computer vision-based support system for B-line detection in lung ultrasound.Frontiers in digital health · 2026Article
- Optimizing transducer choice for the detection of alveolar-interstitial syndrome in dogs and cats: an evidence-based review.Frontiers in veterinary science · 2026Review
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
- Evaluating deep learning approaches for AI-assisted lung ultrasound diagnosis: an international multi-center and multi-scanner study.The ultrasound journal · 2025Article
- Semi-supervised Ensemble Learning for Automatic Interpretation of Lung Ultrasound Videos.Journal of imaging informatics in medicine · 2025Article
- Enhanced gallbladder cancer detection via active and self-supervised learning integration: Innovating B-ultrasound image analysis.PloS one · 2025Article
- Deep learning for accurate B-line detection and localization in lung ultrasound imaging.Frontiers in artificial intelligence · 2025Article
- Thoracic Ultrasound in Others Scenarios: An Expanding Tool.Open respiratory archives · 2024Review
- New perspectives on the use of artificial intelligence in the ultrasound evaluation of lung diseases.Journal of ultrasound · 2024Article
- Comparison of pulmonary congestion severity using artificial intelligence-assisted scoring versus clinical experts: A secondary analysis of BLUSHED-AHF.European journal of heart failure · 2023Article
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F
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Registered trials
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