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Computer vision-based support system for B-line detection in lung ultrasound
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DOI：10.3389/fdgth.2026.1917310.png)
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Abstract
En 中文
Context and objectivesLung ultrasound (LUS) is a safe and cost-effective diagnostic tool. B-lines are fundamental ultrasound (US) artifacts for LUS-based diagnosis of various pulmonary conditions; especially for the evaluation of the lung parenchyma. However; the challenges in their identification and interpretation; combined with a shortage of experts and training programs; restrict the use of this tool in routine clinical practice.MethodsTo overcome these limitations; this study presents a computer vision (CV)-based system for automatic B-line detection. The proposed system integrates preprocessing and feature engineering stages with an object detection module. First; LUS images are normalized to ensure interoperability across different US devices and settings. Subsequently; the Radon and inverse Radon transforms are applied to generate a mask that highlights hyperechoic vertical structures; which is then fused with the preprocessed LUS image. Finally; the resulting image serves as input for a convolutional neural network (CNN) based on the You Only Look Once (YOLO) architecture; enabling the automatic localization of B-lines.ResultsThe results obtained on the test set demonstrate satisfactory performance; achieving a precision of 89.13%; a recall of 80.39%; and an average precision (AP) of 0.82 at an Intersection over Union (IoU) of 0.5.ConclusionsA clinical decision support tool is proposed; aimed at improving efficiency and consistency in LUS interpretation; as well as facilitating its integration into clinical practice. Although the system is still in its conceptual stage; these findings lay the groundwork for future clinical validation processes directed toward its future implementation.
Keywords:
computer vision
object detection
lung ultrasound
YOLO
B-lines
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