# Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning
## 5 Discussion  
The experimental results highlight the importance of using multi-modality data for detecting ARD. Figure 5 presents the average accuracy, precision, recall, and \(F_{1}\) score of the ARD detection system using different modality and different modality fusion schemes. It was found that the depth modality lacks the necessary information required for detecting chest retraction signs, a critical indicator of ARD. This limitation is primarily due to the low resolution of the depth camera (1 megapixel), which struggles to capture fine details, such as the subtle changes in lung pressure associated with chest retraction. Consequently, models relying on the depth modality face difficulties in learning crucial low-level, task-specific features. All three video analysis algorithms support the conclusion that the model struggles to perform with the depth modality alone (Table 1). However, networks using  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Age Group** & **Accuracy** & **Precision** & **Recall** & **TP\_Rate** & **TN\_Rate** \\ \hline
1 & 0.788 & 0.863 & 0.767 & 0.767 & 0.820 \\ \hline
2 & 0.744 & 0.166 & 0.400 & 0.400 & 0.796 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance across age groups (1: <6 years, 2: >=6 years).  
the depth modality were able to make predictions based on the motion features caused by patient restlessness. However, these motion features are not discriminative, as they appeared in both non-ARD and ARD cases, limiting their value for distinguishing between the two conditions.  
Secondly, the experimental outcomes of integrating RGB-D data through early fusion scheme is even worse than using the RGB modality alone. A primary reason for the poor performance is the limited data size, which caused the model to overfit quickly, resulting in suboptimal performance. And, the re-initialization weights of the first convolutional layer of the network limited the potential benefits of transfer learning. In contrast, the integration of RGB-D information through late fusion schemes demonstrated significantly improved performance over single-modality approaches. Specifically, the FCAT-F approach emerged as the most effective fusion strategy in this study, achieving the highest average accuracy and \(F_{1}\) score across all other fusion methods. This improvement is indicative of the benefits of independently training separate models for each modality. By doing so, each model is able to learn more distinct and task-specific features before combining them, leading to a more comprehensive and effective feature representation. This contrasts with end-to-end trained two-stream models (FCAT & SA), where feature learning may be less specialized. In summary, this study shows that using multi-modality information with an effective feature fusion scheme significantly improves ARD detection system performance.  
Figure 5: Performance comparison of ARD detection (X3D) system using different modality and different modality fusion schemes. The bars represent the average performance, with error bars indicating the min-max range of each metric across five folds.