Computer vision-based support system for B-line detection in lung ultrasound.

Context and objectives: Lung 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. Methods: To 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. Results: The 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. Conclusions: A 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.