ArticleEmergency radiology2025
The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.
Article in Emergency radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Role of artificial intelligence and point of care ultrasound in management of critically ill patients.World journal of critical care medicine · 2026Review
- AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026Review
- Lung ultrasound B-lines predict adverse clinical outcomes in adults with sepsis in a resource-limited emergency department.Frontiers in medicine · 2026Article
- Finite element modeling of human thorax for electrical bioimpedance based monitoring of pulmonary fluid accumulation.Journal of electrical bioimpedance · 2026Article
- Applications and indications of point-of-care ultrasound in emergency department encounters involving palliative care patients.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeB-lines in lung ultrasound have been a critical clue for detecting pulmonary edema. However, distinguishing B-lines from other artifacts is a challenge, especially for novice point of care ultrasound (POCUS) practitioners. This study aimed to determine the efficacy of automatic detection of B-lines using artificial intelligence (Auto B-lines) for detecting pulmonary edema.
methodsA retrospective study was conducted on dyspnea patients treated at the emergency department between January 2023 and June 2024. Ultrasound documentation and electronic emergency department medical records were evaluated for sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of auto B-lines in detection of pulmonary edema.
resultsSixty-six patients with a final diagnosis of pulmonary edema were enrolled, with 54.68% having positive B-lines in lung ultrasound. Auto B-lines had 95.6% sensitivity (95% confidence interval [CI]: 0.92-0.98) and 77.2% specificity (95% CI: 0.74-0.80). Physicians demonstrated 82.7% sensitivity (95% CI: 0.79-0.97) and 63.09% sensitivity (95% CI: 0.58-0.69).
conclusionThe auto B-lines were highly sensitive in diagnosing pulmonary edema in novice POCUS practitioners. The clinical integration of physicians and artificial intelligence enhances diagnostic capabilities.
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