## 3 Results  
The demographic and clinical characteristics of the subjects are summarized in Table 1, which compares the group of 145 subjects with ABE to the group of 140 subjects with non-ABE. When considering the gender distribution, the ABE group had 79 males and 66 females, while the non-ABE group consisted of 85 males and 55 females. However, the difference in the gender ratio between the groups was not statistically significant, as indicated by a \(p\) value of 0.287 ([PERSON]'s Chi-Squared test). The average weight of the subjects in the ABE group was 3.78 \(\pm\) 0.68 kg, slightly lower than the average weight of 3.98 \(\pm\) 1.06 kg in the non-ABE group, but this difference was not statistically significant (\(p\) = 0.138, Mann-Whitney U test). A significant difference was observed in the age of the subjects, with the ABE group exhibiting a younger age profile (9.89 \(\pm\) 5.67 days) compared to the non-ABE group (15.76 \(\pm\) 11.69 days). The age difference was statistically significant (\(p\) < 0.001, Mann-Whitney U test). In the assessment of the single-modality MRI data, the incorporation of an attention module (AM) resulted in a significant improvement in all the performance metrics for both the T1- and T2-weighted images (see Table 2). Specifically, the application of the attention module on the T1-weighted MRI resulted in an accuracy of 0.639 \(\pm\) 0.04, an AUC of 0.682 \(\pm\) 0.037, and a sensitivity of 0.688 \(\pm\) 0.09. For the T2-weighted images, the performance was even more significant, with an accuracy of 0.738 \(\pm\) 0.039 and an AUC reaching 0.796 \(\pm\) 0.025. This improvement suggests that the attention module offers a significant advantage in interpreting MRI data. In our multi-modal experiment, we rigorously evaluated five distinct models for classifying combined T1 and T2 MRI modalities (Table 3). These models encompassed various fusion strategies and attention mechanisms to optimize the classification accuracy. We first examined the \"None (Early Fusion)\" model, following [PERSON] et al. 's early fusion strategy [22], which concatenates T1 and T2 modalities before inputting into the model. This baseline approach achieved an accuracy of 0.648 \(\pm\) 0.043, a sensitivity of 0.709 \(\pm\) 0.078, and a specificity of 0.586 \(\pm\) 0.048. The subsequent integration of attention modules (AMs) into the early fusion framework, denoted as \"AM (Early Fusion)\", yielded noticeable improvements across all the metrics, with an accuracy of 0.663 \(\pm\) 0.046, a sensitivity of 0.714 \(\pm\) 0.103, and a specificity of 0.611 \(\pm\) 0.062. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Clinical Features** & **ABE (n = 145)** & **Non-ABE (n = 140)** & \(p\) **Value** \\ \hline Gender & & & & \\ (male/female) & 79/66 & 85/55 & 0.287 1  \\ Weight (kg) & 3.78 \(\pm\) 0.68 & 3.98 \(\pm\) 1.06 & 0.138 2 \\ Age (days) & 9.89 \(\pm\) 5.67 & 15.76 \(\pm\) 11.69 & \textless{}0.001 2  \\ Age (days) & 9.89 \(\pm\) 5.67 & 15.76 \(\pm\) 11.69 & \textless{}0.001 2  \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The demographic and clinical characteristics of the subjects. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Module** & \
\begin{tabular}{c} **MRI** \\ **Modality** \\ \end{tabular} & **Accuracy** & **AUC** & **Sensitivity** & **Specificity** &
\begin{tabular}{c} **Precision** \\ **Score** \\ \end{tabular} & **F1 Score** \\ \hline None & T1 & 0.606 \(\pm\) 0.041 & 0.636 \(\pm\) 0.047 & 0.655 \(\pm\) 0.114 & 0.555 \(\pm\) 0.123 & 0.609 \(\pm\) 0.046 & 0.624 \(\pm\) 0.057 \\ None & T2 & 0.688 \(\pm\) 0.034 & 0.736 \(\pm\) 0.04 & 0.779 \(\pm\) 0.087 & 0.593 \(\pm\) 0.065 & 0.666 \(\pm\) 0.03 & 0.716 \(\pm\) 0.042 \\ AM & T1 & 0.639 \(\pm\) 0.04 & 0.682 \(\pm\) 0.037 & 0.688 \(\pm\) 0.09 & 0.588 \(\pm\) 0.068 & 0.634 \(\pm\) 0.034 & 0.657 \(\pm\) 0.052 \\ AM & T2 & 0.738 \(\pm\) 0.039 & 0.796 \(\pm\) 0.025 & 0.764 \(\pm\) 0.083 & 0.711 \(\pm\) 0.048 & 0.733 \(\pm\) 0.029 & 0.746 \(\pm\) 0.048 \\ \hline \hline \end{tabular}
\end{table}