Observational studyJournal of ultrasound2025
Automatic approach for B-lines detection in lung ultrasound images using You Only Look Once algorithm.
Observational study in Journal of ultrasound, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 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
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeB-lines are among the key artifact signs observed in Lung Ultrasound (LUS), playing a critical role in differentiating pulmonary diseases and assessing overall lung condition. However, their accurate detection and quantification can be time-consuming and technically challenging, especially for less experienced operators. This study aims to evaluate the performance of a YOLO (You Only Look Once)-based algorithm for the automated detection of B-lines, offering a novel tool to support clinical decision-making. The proposed approach is designed to improve the efficiency and consistency of LUS interpretation, particularly for non-expert practitioners, and to enhance its utility in guiding respiratory management.
methodsIn this observational agreement study, 644 images from both anonymized internal and clinical online database were evaluated. After a quality selection step, 386 images remained available for analysis from 46 patients. Ground truth was established by blinded expert sonographer identifying B-lines within rectangular Region Of Interest (ROI) on each frame. Algorithm performances were assessed through Precision, Recall and F1 Score, whereas to quantify the agreement between the YOLO-based algorithm and the expert operator, weighted kappa (kw) statistics were employed.
resultsThe algorithm achieved a precision of 0.92 (95% CI 0.89-0.94), recall of 0.81 (95% CI 0.77-0.85), and F1-score of 0.86 (95% CI 0.83-0.88). The weighted kappa was 0.68 (95% CI 0.64-0.72), indicating substantial agreement algorithm and expert annotations.
conclusionsThe proposed algorithm has demonstrated its potential to significantly enhance diagnostic support by accurately detecting B-lines in LUS images.
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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.