ArticleThe ultrasound journal2021
B-line quantification: comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review.
Article in The ultrasound journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06377449 (Influence of Ultrasound Examination of the Lungs on the Prognosis and Postoperative Outcomes in Cardiac Surgical Patients), which is not on this map. Cited by 23 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Influence of Ultrasound Examination of the Lungs on the Prognosis and Postoperative Outcomes in Cardiac Surgical Patients
Who cites it
23 citing papers in PubMed, 41 citations in OpenAlex.
- Automatic approach for B-lines detection in lung ultrasound images using You Only Look Once algorithm.Journal of ultrasound · 2025Observational
- Evolving role of point-of-care ultrasound in prehospital emergency care: a narrative review.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Review
- Ten Questions on Using Lung Ultrasonography to Diagnose and Manage Pneumonia in Hospital-at-Home Model: Part II-Confounders and Mimickers.Diagnostics (Basel, Switzerland) · 2025Review
- The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.Emergency radiology · 2025Article
- Performance of a point-of-care ultrasound platform for artificial intelligence-enabled assessment of pulmonary B-lines.Cardiovascular ultrasound · 2025Article
- Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.JAMA cardiology · 2025Article
- Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study.Frontiers in veterinary science · 2025Article
- Automatic B-lines: a tool for minimizing time to diuretic administration in pulmonary edema patients in the emergency department of a developing country.International journal of emergency medicine · 2024Article
- Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines.Diagnostics (Basel, Switzerland) · 2024Article
- A machine learning-based lung ultrasound algorithm for the diagnosis of acute heart failure.Internal and emergency medicine · 2024Article
- Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure.The ultrasound journal · 2024Article
- To B or not to B-lines.Journal of anesthesia, analgesia and critical care · 2024Article
- Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review.JACC. Advances · 2024Article
- Lung ultrasound and supine chest X-ray use in modern adult intensive care: mapping 30 years of advancement (1993-2023).The ultrasound journal · 2024Review
- Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses.Anaesthesiology intensive therapy · 2024Review
- The value of lung ultrasound score in neonatal respiratory distress syndrome: a prospective diagnostic cohort study.Frontiers in medicine · 2024Article
- Advancing healthcare through thoracic ultrasound research in older patients.Aging clinical and experimental research · 2023Review
- Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.IEEE journal of biomedical and health informatics · 2023Article
- The Incremental Role of Multiorgan Point-of-Care Ultrasounds in the Emergency Setting.International journal of environmental research and public health · 2023Review
- Automated lung ultrasound image assessment using artificial intelligence to identify fluid overload in dialysis patients.BMC nephrology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe goal of this study was to assess the ability of machine artificial intelligence (AI) to quantitatively assess lung ultrasound (LUS) B-line presence using images obtained by learners novice to LUS in patients with acute heart failure (AHF), compared to expert interpretation.
methodsThis was a prospective, multicenter observational study conducted at two urban academic institutions. Learners novice to LUS completed a 30-min training session on lung image acquisition which included lecture and hands-on patient scanning. Learners independently acquired images on patients with suspected AHF. Automatic B-line quantification was obtained offline after completion of the study. Machine AI counted the maximum number of B-lines visualized during a clip. The criterion standard for B-line counts was semi-quantitative analysis by a blinded point-of-care LUS expert reviewer. Image quality was blindly determined by an expert reviewer. A second expert reviewer blindly determined B-line counts and image quality. Intraclass correlation was used to determine agreement between machine AI and expert, and expert to expert.
resultsFifty-one novice learners completed 87 scans on 29 patients. We analyzed data from 611 lung zones. The overall intraclass correlation for agreement between novice learner images post-processed with AI technology and expert review was 0.56 (confidence interval [CI] 0.51-0.62), and 0.82 (CI 0.73-0.91) between experts. Median image quality was 4 (on a 5-point scale), and correlation between experts for quality assessment was 0.65 (CI 0.48-0.82).
conclusionAfter a short training session, novice learners were able to obtain high-quality images. When the AI deep learning algorithm was applied to those images, it quantified B-lines with moderate-to-fair correlation as compared to semi-quantitative analysis by expert review. This data shows promise, but further development is needed before widespread clinical use.
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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.