# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 5 Conclusions and Future Research  
CNNs are suitable for lung sliding detection from clinical LUS videos. Our architecture, automated M-mode classification, achieved a balanced accuracy of 86-89% with its 16-64-frame versions. There were no significant differences in classification performance between the proposed lung and centroid slice selection methods in the best-performing classifiers. M-mode images are 2D temporal slices that form seashoer or barcode patterns. These patterns are a key feature for the classification of M-mode images. In our opinion, physicians' familiarity with these patterns and their simplicity marks the direction for future application in clinical practice. Furthermore, automatic selection of the M-mode slice simplifies the detection of the absence of lung sliding and relieves a physician from having to perform manual slice selection.  
Our methods utilise black-box classifiers, CNNs, that implicitly do not provide any explanation. As a part of future research, we plan to visualise the input data by highlighting the areas that contribute the most to the CNN's decisions and by creating a more elaborate decision support system. Furthermore, explainability methods for CNNs such as [20; 21; 22] could provide valuable insight to reinforce or contradict the physician's decision. Following the application of 2D CNNs for lung sliding absence detection from M-mode images, we want to detect lung sliding directly from B-mode images with 3D CNNs. We plan to expand the dataset size to make it more applicable to the training of 3D CNNs. Furthermore, our dataset currently contains only two classes: Presence and absence of lung sliding. The absence of lung sliding patterns does not, by itself, provide sufficient information for pneumothorax detection. Therefore, the next logical step is the detection of lung point, which confirms pneumothorax. In addition, the detection and localisation of A-line and B-line artifacts may provide more insight into the correlation between lung sliding motion, the position of lung point, and these artifacts.  
Conceptualisation, M.J., M.B., M.M., A.D., N.F., and F.B.; Data curation, M.M. and A.D.; Formal analysis, M.B., A.D., and F.B.; Funding acquisition, M.B. and F.B.; Investigation, M.B.; Methodology, M.J., M.B., and M.M.; Resources, M.J. and M.M.; Software, M.J. and N.F.; Supervision, M.B. and A.D.; Validation, M.M.; Visualisation, N.F.; Writing--original draft, M.J.; Writing--review & editing, M.J. and M.B. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Slovak Research and Development Agency under Grant No. APVV-20-0232 and Grant No. APVV-16-0213.  
All procedures performed in studies involving human participants were in accordance with the ethical standards approved by the ethical committee of Martin University Hospital, and by the ethical committee of Jessenius Faculty of Medicine in Martin under No. EK 2015/2017, and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Dataset for the semantic segmentation, dataset for the detection of lung sliding motion and code are available at [[https://github.com/Miro001/auto-mmode-classification](https://github.com/Miro001/auto-mmode-classification)]([https://github.com/Miro001/auto-mmode-classification](https://github.com/Miro001/auto-mmode-classification)). Accessed on 28 July 2021.  
The authors declare no conflict of interest.