Automatic approach for B-lines detection in lung ultrasound images using You Only Look Once algorithm

B-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.

In 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.

The 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.

The proposed algorithm has demonstrated its potential to significantly enhance diagnostic support by accurately detecting B-lines in LUS images.