# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
## 6 Conclusions  
Lung ultrasound as an established diagnosis tool that is both safe and highly available constitutes a method with potentially huge impact that has nevertheless been neglected by the machine learning community. This work presents a novel LUS dataset for COVID-19 alongside new methods and analyses that pave the way towards computer vision-assisted differential diagnosis of COVID-19 from US. We provide an extensive analysis of interpretable methods that are relevant not only in the context of COVID-19, but in general for the diagnosis of viral and bacterial pneumonias. Our published database is constantly updated and verified by medical experts and researchers are invited to contribute to our initiative. We strongly believe in the value of such open-source databases not only for follow-up work, but also for related fields. Our data in particular can be utilized for pre-training (transfer learning) in other US applications [41], as done in Model Genesis[73]. Even research on noise and artifact removal from US [81], or US image simulation [82] might benefit from the availability of US data. The available metadata also opens up opportunities for further analysis with respect to pre-conditions, symptoms and patterns, as we touched upon in Figure 2a,b. We envision the proposed tool as a step toward a decision support system to aid diagnosis by providing a \"second opinion\" to increase reliability. The promising results of our model are to be validated in a controlled clinical study that investigates the predictive power of US for automatic detection of COVID-19, especially in comparison to CT and CXR. **Author Contributions:** Conceptualization, J.B., N.W., M.C., G.B. and J.G. ; methodology, J.B., N.W. and G.B. ; software, J.B. and N.W. ; validation, C.B., G.B., A.A., B.R. and K.B. ; formal analysis, J.B. and N.W. ; investigation, J.B. and N.W. ; resources, J.B.; data curation, N.W., M.C., C.B., G.B., K.L. and J.G. ; writing--original draft preparation, J.B., N.W. and M.C. ; writing--review and editing, J.B., N.W., M.M., B.R. and K.B. ; visualization, J.B. and N.W. ; supervision, B.R. and K.B. ; project administration, B.R. and K.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was funded in part by the Alfried Krupp Prize for Young University Teachers of the Alfried Krupp von Bohlen und Halbach-Stiftung (K.B.). **Institutional Review Board Statement:** For the data collected in Northumbria, local caldicott approval was sought and granted with reference C3424. **Informed Consent Statement:** Informed consent was obtained from all subjects from the Northumbria cohort. Consent was also obtained from the Neuruppin cohort of healthy controls. For data collected from public sources and related works, we must refer to the respective source for information on patient consent (see Table A1). **Data Availability Statement:** All data presented used in this study are openly available from: [[https://github.com/BorgwardtLab/covid19_ultrasound](https://github.com/BorgwardtLab/covid19_ultrasound)]([https://github.com/BorgwardtLab/covid19_ultrasound](https://github.com/BorgwardtLab/covid19_ultrasound)). US recordings that were retrieved and processed from third party platforms (ThePocusAtlas, LIFTL, GrepMed, Radiopaedia.org) are available via Creative Commons License. The data from Butterfly was not available for non-commercial redistribution but can be obtained automatically with a script from our repository. The software code used to train the models and evaluate the results is also available at: [[https://github.com/BorgwardtLab/covid19_ultrasound](https://github.com/BorgwardtLab/covid19_ultrasound)]([https://github.com/BorgwardtLab/covid19_ultrasound](https://github.com/BorgwardtLab/covid19_ultrasound)). **Acknowledgments:** The authors would like to thank [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for help in data collection, useful discussions and feedback on the manuscript. Moreover, we thank [PERSON] for sharing pre-trained segmentation models.