ArticleMedical & biological engineering & computing2023
Automated detection and localization of pericardial effusion from point-of-care cardiac ultrasound examination.
Article in Medical & biological engineering & computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- AI-assisted ultrasound for trauma: a scoping review.BMC medical imaging · 2026Pooled it
- Diagnostic accuracy of AI-assisted point-of-care ultrasound for abdominal free fluid detection in FAST trauma assessment: a systematic review and meta-analysis.BMC emergency medicine · 2026Pooled it
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
- Artificial Intelligence in Cardiac Point-of-Care Ultrasound: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Role of artificial intelligence and point of care ultrasound in management of critically ill patients.World journal of critical care medicine · 2026Review
- A Lightweight Network for Free Fluid Detection in Focused Assessment with Sonography in Trauma (FAST) Examination.Bioengineering (Basel, Switzerland) · 2026Article
- Point-of-Care Ultrasound in Blunt Chest Trauma: Diagnostic Performance and Clinical Relevance.Cureus · 2026Review
- Article
- AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026Review
- Automated evaluation for pericardial effusion and cardiac tamponade with echocardiographic artificial intelligence.European heart journal. Digital health · 2025Article
- Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.Current atherosclerosis reports · 2025Review
- Evaluating the digital health technology landscape in sub-Saharan Africa and its implications for cardiovascular health.NPJ cardiovascular health · 2025Review
- Ultrasound findings of free fluid in non-traumatic acute abdomen: a prospective case series study.BMC surgery · 2025Article
- Ultrasonographic Applications of Novel Technologies and Artificial Intelligence in Critically Ill Patients.Journal of personalized medicine · 2024Review
- Automated detection and segmentation of pleural effusion on ultrasound images using an Attention U-net.Journal of applied clinical medical physics · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Focused Assessment with Sonography in Trauma (FAST) exam is the standard of care for pericardial and abdominal free fluid detection in emergency medicine. Despite its life saving potential, FAST is underutilized due to requiring clinicians with appropriate training and practice. To aid ultrasound interpretation, the role of artificial intelligence has been studied, while leaving room for improvement in localization information and computation time. The purpose of this study was to develop and test a deep learning approach to rapidly and accurately identify both the presence and location of pericardial effusion on point-of-care ultrasound (POCUS) exams. Each cardiac POCUS exam is analyzed image-by-image via the state-of-the-art YoloV3 algorithm and pericardial effusion presence is determined from the most confident detection. We evaluate our approach over a dataset of POCUS exams (cardiac component of FAST and ultrasound), comprising 37 cases with pericardial effusion and 39 negative controls. Our algorithm attains 92% specificity and 89% sensitivity in pericardial effusion identification, outperforming existing deep learning approaches, and localizes pericardial effusion by 51% Intersection Over Union with ground-truth annotations. Moreover, image processing demonstrates only 57 ms latency. Experimental results demonstrate the feasibility of rapid and accurate pericardial effusion detection from POCUS exams for physician overread.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.