Frequency-Aware B-Line and Pleural Line Analysis in Lung Ultrasound Videos

Accurately identifying B-lines and pleural line (P-line) in lung ultrasound (LUS) videos is valuable for evaluating certain lung conditions. However, manual interpretation remains subjective and highly dependenton operator expertise. Existing deep learning methods often suffer from performance degradation due to speckle noise and motion artifacts. Moreover, the limited availability of LUS video data annotated for multiple diagnostic features such as B-lines and the P-line limits model development. Therefore, this paper introduces ILD-LUS, a new clinical LUS database designed based on interstitial lung disease (ILD) analysis by category labeling, comprising 2,149 ultrasound videos (193,410 frames). Also, we construct an external test set based on the public Covid-BLUES dataset for the evaluation of B-lines and P-line recognition in different pulmonary pathologies. Then, we propose a novel video analysis framework that integrates wavelet enhancement with temporal attention modeling. Specifically, we employ a dual-component frequency feature enhancement method using the Discrete Wavelet Transform (DWT), which effectively suppresses noise while preserving important landmarks. Subsequently, an adaptive attention module is introduced to model long-range temporal dependencies and improve dynamic feature representation across consecutive frames. Experimental results show that the proposed method achieves over 94% AUC and 82% ACC for both B-lines and P-line classification on both the ILD-LUS and Covid-BLUES datasets, outperforming existing methods. These findings demonstrate the robustness and generalizability of our approach across different pathological conditions. Overall, the proposed framework shows strong potential for supporting clinical decision-making in LUS analysis.