## 3 Results
### Single-Source vs. Multi-Source Training
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensitivity** & **CheXpert** & **MIMIC-CXR** & **NIH** & **PadChest** & **SIIM-ACR** & **VinDR-CXR** & **Multi-Source (All)** \\ \hline \multirow{2}{*}{BRAX} & 0.032 & 0.085 & **0.13** & 0.044 & 0.024 & 0.003 & 0.085 \\  & (0.025-0.038) & (0.066-0.103) & (0.1–0.161) & (0.031-0.056) & (0.02–0.029) & (0.001–0.006) & (0.067–0.102) \\ \hline \multirow{2}{*}{CANDID-PTX} & 0.494 & **0.784** & 0.674 & 0.682 & 0.27 & 0.555 & 0.724 \\  & (0.481-0.506) & (0.771-0.799) & (0.658-0.689) & (0.662-0.702) & (0.262-0.278) & (0.529–0.579) & (0.71–0.739) \\ \hline \multirow{2}{*}{CRADI} & 0.061 & 0.271 & **0.322** & 0.117 & 0.057 & 0.173 & 0.27 \\  & (0.052-0.069) & (0.237-0.306) & (0.281-0.366) & (0.098-0.136) & (0.049–0.066) & (0.108–0.242) & (0.236–0.304) \\ \hline \multirow{2}{*}{Indiana} & 0.056 & **0.105** & 0.09 & 0.043 & 0.02 & 0.096 & 0.078 \\  & (0.037–0.077) & (0.06–0.159) & (0.051–0.129) & (0.019–0.066) & (0.012–0.028) & (0.032–0.173) & (0.048–0.114) \\ \hline \multirow{2}{*}{Micro-Average} & 0.329 & **0.449** & 0.443 & 0.377 & 0.274 & 0.365 & 0.431 \\  & (0.093-0.751) & (0.031–0.867) & (0.065-0.831) & (0.062–0.816) & (0.144–0.693) & (0.047–0.778) & (0.019–0.844) \\ \hline \multirow{2}{*}{**Overall**} & 0.298 & **0.594** & 0.588 & 0.408 & 0.187 & 0.297 & 0.569 \\  & (0.29–0.305) & (0.58–0.608) & (0.573–0.602) & (0.395–0.422) & (0.181–0.192) & (0.28–0.311) & (0.556–0.58) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: F1-score results for external test sets using single-source and multi-source training datasets. Best results in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensitivity** & **CheXpert** & **MIMIC-CXR** & **NIH** & **PadChest** & **SIIM-ACR** & **VinDR-CXR** & **Multi-Source (All)** \\ \hline \multirow{2}{*}{CRADI} & 0.905 & 0.836 & 0.726 & 0.612 & 0.851 & 0.094 & **0.906** \\  & (0.861–0.942) & (0.785–0.884) & (0.665–0.784) & (0.547–0.672) & (0.803–0.898) & (0.056–0.136) & (0.865–0.944) \\ \hline \multirow{2}{*}{Indiana} & **0.931** & 0.573 & 0.574 & 0.357 & 0.823 & 0.251 & 0.751 \\  & (0.823–1.0) & (0.385–0.75) & (0.387–0.75) & (0.2–0.524) & (0.667–0.957) & (0.097–0.421) & (0.579–0.897) \\ \hline \multirow{2}{*}{PTX498} & **0.955** & 0.794 & 0.663 & 0.556 & 0.844 & 0.348 & 0.871 \\  & (0.938–0.976) & (0.757–0.831) & (0.62–0.707) & (0.512–0.598) & (0.809–0.876) & (0.305–0.39) & (0.839–0.9) \\ \hline \multirow{2}{*}{Micro-Average} & **0.845** & 0.677 & 0.600 & 0.453 & 0.814 & 0.193 & 0.782 \\  & (0.658–1.032) & (0.521–0.833) & (0.456–0.745) & (0.316–0.591) & (0.704–0.925) & (0.065–0.321) & (0.617–0.948) \\ \hline \multirow{2}{*}{**Overall**} & **0.908** & 0.723 & 0.666 & 0.475 & 0.899 & 0.238 & 0.863 \\  & (0.898–0.916) & (0.709–0.737) & (0.651–0.68) & (0.46–0.489) & (0.89–0.909) & (0.225–0.251) & (0.852–0.873) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensitivity** & **CheXpert** & **MIMIC-CXR** & **NIH** & **PadChest** & **SIIM-ACR** & **VinDR-CXR** & **Multi-Source (All)** \\ \hline \multirow{2}{*}{BRAX} & 0.06 & 0.143 & **0.191** & 0.076 & 0.047 & 0.006 & 0.146 \\  & (0.048–0.072) & (0.114–0.171) & (0.151–0.233) & (0.055–0.096) & (0.038–0.055) & (0.001–0.012) & (0.118–0.173) \\ \hline \multirow{2}{*}{CANDID-PTX} & 0.298 & **0.594** & 0.588 & 0.408 & 0.187 & 0.297 & 0.569 \\  & (0.29–0.305) & (0.58–0.608) & (0.573–0.602) & (0.395–0.422) & (0.181–0.192) & (0.28–0.311) & (0.556–0.58) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Specificity results for external test sets using single-source and multi-source training datasets. Best results in bold. ROC and PR curves were also calculated for each single-source training dataset independently, as shown in Figures 6 and 7. The results show that the model trained with multi-source data is better balanced and has higher generalization capabilities to unseen data compared to the models trained with single-source data.