## 3 Results
### HE Classification Based on CNN Classification Model  
Table 3 lists the AUC (95% CI) of the CNN classification model using inputs from baseline and follow-up segmentation masks and original head CT scans (Figure 1) versus classification based on automated segmentation volume alone. The AUC of the CNN models predicting \(\geq\)3, \(\geq\)9, and \(\geq\)12.5 mL HE in the ATACH-2 cohort and \(\geq\)3 mL HE in the Charite dataset were higher than HE classification using the automated segmentation volumes alone. However, the main advantage of CNN classification models was their ability to establish thresholds that minimize false-negative or false-positive rates, while limiting expert review of segmentations to test-positive or test-negative subjects, respectively. We determined the thresholds for 100%, 95%, and 90% sensitivity and specificity in predicting \(\geq\)3, \(\geq\)6, \(\geq\)9, and \(\geq\)12.5 mL HE among the internal test cohort (ATACH-2) and evaluated their performance in the external validation cohort (Charite) (Figures 3-6). High-sensitivity thresholds minimize false-negative rates, thereby limiting expert review of automated segmentations to test-positive subjects. Conversely, high-specificity thresholds minimize false-positive rates, limiting segmentation reviews to test-negative subjects. Across different HE definitions in the ATACH-2 and Charite cohorts, a 100% sensitivity threshold excluded 14% to 77% of subjects from expert review of automated segmentations (negative prediction), allowing less than 1% false-negative HE misclassification. Conversely, a 100% specificity threshold excluded only 2% to 7% of subjects from expert review of segmentations. High-sensitivity models appear to perform more efficiently in excluding subjects from expert review, while maintaining low false-negative HE misclassification. A more practical threshold might be 95% sensitivity, which could exclude 46% to 88% of subjects from expert review while allowing less than 2% false-negative HE misclassification (Figures 3-6). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Internal Independent Test (ATACH-2, \(n\) = 890)**} & \multicolumn{3}{c}{**External Independent Test (Charite, \(n\) = 687)**} \\ \hline  & **Classification by CNN Model** & **Classification Segmentation Volumes** & \(p\) **Value** & **Classification by CNN Model** & **Classification Based on Segmentation Volumes** & \(p\) **Value** \\ \hline \(\geq\)**3 mL HE** & 0.93 (0.90–0.95) & 0.86 (0.84–0.88) & 0.005 & 0.9 (0.87–0.92) & 0.82 (0.79–0.85) & 0.018 \\ \(\geq\)6 mL HE & 0.95 (0.94–0.97) & 0.89 (0.87–0.91) & 0.175 & 0.93 (0.90–0.95) & 0.88 (0.86–0.90) & 0.520 \\ \(\geq\)9 mL HE & 0.97 (0.95–0.98) & 0.89 (0.87–0.91) & 0.022 & 0.93 (0.91–0.95) & 0.88 (0.86–0.92) & 0.525 \\ \(\geq\)12.5 mL HE & 0.98 (0.98–0.99) & 0.92 (0.90–0.94) & 0.037 & 0.95 (0.93–0.98) & 0.87 (0.85–0.99) & 0.882 \\ \hline \hline \end{tabular}  
* The ROC AUC (95% CI) of HE classification based on CNN classification versus directly from automated segmentation volumes.  
\end{table} 
Table 3: Comparison of HE classification based on CNN classification model versus direct subtraction of hematoma volumes from automated segmentation. Figure 3: Classification of \(\geq\)3 mL HE using CNN model and thresholds for 100%, 95%, and 90% sensitivity and specificity, as well as the highest accuracy threshold, in the internal test cohort (ATACH-2). These thresholds were then applied to the external validation cohort (Charite). The solid and dashed lines in the ROC curve refer to same-color sensitivity/specificity thresholds (as color coded in table cell) in the internal and external validation cohorts, respectively. Figure 4: Classification of \(\geq\)6 mL HE using CNN model and thresholds for 100%, 95%, and 90% sensitivity and specificity, as well as the highest accuracy threshold, in the internal test cohort (ATACH-2). These thresholds were then applied to the external validation cohort (Charite).