# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 6 Discussion and Conclusions  
We have proposed a spatiotemporal attention based convolutional+LSTM model capable of detecting the B-line artefacts and localizing them both spatially and temporally within LUS videos. Besides boosting the B-line classification accuracy, our spatiotemporal mechanism can increase the model interpretability, by suggesting when and where B-line artefacts are found. This localization ability was demonstrated quantitatively and qualitatively, despite being trained in a weakly-supervised manner. The model showed a B-line classification \(F1\) score of 83.2% and a B-line spatial and temporal localization accuracy of \(r=0.67\%\) and IoU = 69.7%, respectively. These results are consistent with qualitative analysis via visual inspection of the resulting attention maps, which highlight frames and regions with the most salient B-lines in the video. In the context of competing methods, as outlined in Section 2, our method achieves state-of-the-art accuracy, and additionally can identify the most relevant frame. Furthermore, compared to a study by [PERSON] et al. (2019), which investigated the agreement between physicians for assessing B-line presence on ultrasound in emergency patients and found intraclass correlation coefficients (ICC) agreement of 0.729, the results of our study for B-line classification (\(F1\) score of 83.2%) is promising for a correct diagnosis. A limitation of this study is that we focused on B-line artefacts only. Although this is the most important artefact in lung assessment, a complete lung examination including other features, such as pleural effusion, consolidation and confluent B-lines, will be incorporated in future work. Our proposed model is a first step towards bringing automated LUS analysis to the ICU, particularly for the management of dengue patients in low and middle income countries where operator expertise is limited. Conceptualization, H.K. and A.G.; methodology, H.K. ; software, H.K. ; validation, H.K. and N.T.H.P. ; formal analysis, H.K. ; investigation, H.K. ; resources, N.T.H.P. ; data curation, H.K., N.T.H.P. and A.M.; writing--original draft preparation, H.K. ; writing--review and editing, H.K., N.T.H.P. and A.G.; visualization, H.K. and A.G.; supervision, S.Y., L.P., L.T. and A.G.; project administration, A.G., R.R., L.T. and S.Y. ; funding acquisition, H.V.N., T.B.D., R.R., L.T., S.Y. and A.G. All authors have read and agreed to the published version of the manuscript. This work was supported by the Wellcome Trust UK (110179/Z/15/Z, 203905/Z/16/Z). [PERSON], [PERSON], [PERSON] and [PERSON] also acknowledge financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Oxford Tropical Research Ethics Committee (OxTREC) Ethics Committee and the HTD Institutional Review Boards. Informed Consent Statement: Informed consent obtained from subjects involved in the study. The data presented in this study are available on reasonable request from the corresponding author. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline C2D+LSTM & 75.1 & 85.0 & 79.7 \\ \hline C2D+S+LSTM & 75.7 & 86.1 & 80.5 \\ \hline C2D+LSTM+T & 76.1 & 86.8 & 81.0 \\ \hline C2D+S+LSTM+T* & 76.8 & 89.8 & 82.7 \\ \hline
**C2D+S+LSTM+T** & 77.2 & 90.3 & **83.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiments of the proposed model (C2D+S+LSTM+T). All the reported values are in percentages.