# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
## 2 A Lung Ultrasound Dataset for COVID-19 Detection  
### Dataset Description  
We release the largest publicly-available LUS dataset ([[https://github.com/BorgwardtLab/covid19_ultrasound](https://github.com/BorgwardtLab/covid19_ultrasound)]([https://github.com/BorgwardtLab/covid19_ultrasound](https://github.com/BorgwardtLab/covid19_ultrasound))), comprising samples of COVID-19 patients, patients with bacterial pneumonia, (non-COVID-19) viral pneumonia and healthy controls. As shown in Table 1, we collected and gathered 261 recordings (202 videos + 59 images) recorded with either convex or linear probes from a total of 216 patients. Linear probes are higher frequency, yielding a greater resolution that allows better to study abnormalities around the pleural line [61]. However, the linear probe penetrates the tissue less than the convex probe which can hamper the differentiation of B-lines [62] and does not allow the assessment of deeper lung tissue. In Section 5.2 we discuss limitations of our dataset regarding the heterogeneity of US probes in the data, as well as shortcomings with respect to the distinction of COVID-19 from other viral pneumonias. Note that due to the low number of samples (3 convex videos), we did not use the non-COVID-19 viral pneumonia data in the analysis, but instead only distinguish the remaining three classes.  
Our dataset comprises clinical data donated from hospitals or academic ultrasound course instructors, as well as LUS recordings published in other scientific literature, community platforms, open medical repositories and health-tech companies. An overview about the sources and a full list of utilized publications is listed in the supplementary material (Table A1). The COVID-19 diagnosis was normally obtained via RT-PCR, but for details on individual recordings, we refer to the respective sources. We consider it a major contribution to assemble this dataset from 41 distinct sources, which included web scraping, labeling, pre-processing (cropping, artifact removal etc.), commenting and approving by medical professionals and US operators. The dataset is accompanied by extensive metadata table, listing the source URL, source ID, an anonymized patient ID and, when available, age, gender, disease symptoms and pathological patterns. Further technical details comprise image resolution, frame rate and the number of frames for each video after pre-processing. Importantly, all samples of our database were reviewed and approved by two medical  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Convex**} & \multicolumn{3}{c}{**Linear**} \\ \hline  & **Vid.** & **Img.** & **Vid.** & **Img.** & **Sum** \\ \hline
**COVID-19** & 64 & 18 & 6 & 4 & **92** \\ \hline
**Bacterial Pneu.** & 49 & 20 & 2 & 2 & **73** \\ \hline
**Viral Pneu.** & 3 & – & 3 & – & **6** \\ \hline
**Healthy** & 66 & 15 & 9 & – & **90** \\ \hline
**Sum** & **182** & **53** & **20** & **6** & **261** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset size. Number of videos and images in our dataset, per class and probe.  
experts (a paediatric physician with 10+ years of clinical LUS experience and an academic US course instructor) and annotated with visible LUS patterns.