## 3 Results
### Evaluation of Multi-Source Model on External Datasets
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Prevalence** & \begin{tabular}{c} **Sensitivity** \\ **(Recall)** \\ \end{tabular} & **Specificity** & \
\begin{tabular}{c} **PPV** \\ **(Precision)** \\ \end{tabular} & **NPV** & **F1-score** & **ROC-AUC** &
\begin{tabular}{c} **AP** \\ **(PR-AUC)** \\ \end{tabular} \\ \hline
**BRAX** & 0.8\% & 0.538 & 0.953 & 0.085 & 0.996 & 0.147 & 0.846 & 0.199 \\
**(185/19/29)** & (0.464-0.617) & (0.949-0.956) & (0.068-0.101) & (0.995-0.997) & (0.119-0.173) & (0.813-0.881) & (0.137-0.264) \\ \hline
**CANDID-PTX** & 16.6\% & 0.874 & 0.929 & 0.71 & 0.974 & 0.783 & 0.959 & 0.892 \\
**(3196/19/237)** & (0.862-0.886) & (0.925-0.933) & (0.695-0.725) & (0.971-0.976) & (0.772-0.794) & (0.955-0.963) & (0.883-0.9) \\ \hline
**CRADI** & 1.9\% & 0.902 & 0.96 & 0.305 & 0.998 & 0.455 & 0.972 & 0.726 \\  & (201/10,440) & (0.859-0.94) & (0.956-0.963) & (0.266-0.342) & (0.997-0.999) & (0.41-0.497) & (0.956-0.984) & (0.661-0.789) \\ \hline
**Indiana** & 0.7\% & 0.751 & 0.941 & 0.086 & 0.998 & 0.154 & 0.899 & 0.536 \\
**(28/38/282)** & (0.579-0.897) & (0.933-0.948) & (0.054-0.126) & (0.996-0.999) & (0.1–0.218) & (0.812-0.967) & (0.344-0.704) \\ \hline
**PTX-498 1** & 100.0\% & 0.881 & - & 1.0 & - & - & - \\  & (498/498) & (0.851-0.91) & - & (1.0–1.0) & - & - & - \\ \hline
**Macro-Average 2** & 7.6\% & 0.789 & 0.946 & 0.437 & 0.992 & 0.385 & 0.919 & 0.588 \\
**(4081/35/426)** & (0.637-0.942) & (0.932-0.959) & (0.032-0.842) & (0.980-1.003) & (0.083-0.687) & (0.861-0.977) & (0.291-0.886) \\ \hline
**Overall 2** **(micro-average)** & 7.6\% & 0.862 & 0.945 & 0.566 & 0.988 & 0.683 & 0.961 & 0.825 \\
**(4081/53/426)** & (0.852-0.872) & (0.943-0.947) & (0.554-0.577) & (0.987-0.989) & (0.673-0.693) & (0.957-0.964) & (0.814-0.834) \\ \hline \hline \end{tabular}  
* Some metrics cannot be computed for PTX-498 dataset due to lack of negative samples. 2 Micro-average is the mean \(\pm\) standard deviation for each metric from all individual datasets. 3 Overall refers to prediction in all datasets combined into one (bigger datasets have more influence).  
\end{table} 
Table 4: Evaluation of the model trained on multi-source data on external datasets. Figure 3: **(a) ROC curve and (b) precision–recall curve for pneumothorax. The thick blue line represents the overall curve (micro-average), and the thin lines are the curves for individual datasets. PTX-498 is not shown due to lack of negative samples. **  
The curves from the individual datasets are shown with the exception of the PTX-498 dataset (which lacks positive samples), and are similar to the curve from the aggregated dataset with the exception of the BRAX and Indiana datasets. The distribution of real and predicted values is shown in Figure 4 as confusion matrices. Percentages are row-normalized, with the number of samples in parentheses. Individual confusion matrices per-dataset are shown along with the confusion matrix for the overall test dataset. Table 5 shows the results of the model evaluation on the other two labels the model was trained on along with pneumothorax: _no_finding_ (which defines normal or pathological images, including many pathologies, pneumothorax amongst them), and _support_devices_ (which include several devices such as tubes, pacemakers, catheters, etc.). Although those labels have no clinical use in our work, they are useful for training because they help the model learn other patterns of disease that can become confounders if they are present along with pneumothorax.