# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 4 Discussion
This improvement arises from the replacement of the 3D U-Net# model with a 3D object detection model for bleeding detection. The U-Net# architecture extends the vanilla 3D U-Net by adding dense and full-scale skip connections. In contrast, the 3D object detection model uses a residual network-based backbone and detection head. As the object detection model does not include a decoder structure like that of U-Net, it achieves reduced inference time. Several studies have reported that the median time from CT examination to diagnosis is approximately 20-30 min [32; 33; 34]. Given that the median time from CT examination to diagnosis has been reported to be approximately 20-30 min [32; 33; 34], our CAD software is considered clinically feasible if it delivers outputs within 20 min when used as a second reader. In contrast, [PERSON] et al. [1] reported a significantly improved 28-day survival rate after shortening the time from arrival at the emergency room to bleeding control procedures. Therefore, further reduction in processing time is essential. The bounding boxes used to train the 3D object detection model were generated from manually defined regions using voxel-wise painting. Generally, defining bounding boxes is significantly less time-consuming than voxel-wise painting [35; 36]. Despite the coarser annotation granularity of bounding boxes compared to voxel-wise paintings, our object detection-based approach demonstrated superior detection performance compared with segmentation-based approaches. This suggests that additional training data can be annotated using bounding boxes alone. Furthermore, this approach may be suitable for detecting fractures that are often challenging to define using voxel-wise painting. The proposed method could be extended to fracture detection by preparing bounding box annotations for the images. Additionally, because object detection models can simultaneously identify multiple types of regions, we plan to develop CAD software capable of detecting both bleeding and fractures. This study has several limitations. First, only delayed-phase contrast-enhanced CT images were used. Although including non-contrast CT images may enhance the detection performance, it would require image registration between contrast-enhanced and non-contrast CT scans. Although various registration methods exist, including rigid, non-rigid, and deep-learning-based approaches, determining the most compatible option for our system architecture remains a challenge. Alternatively, generating pseudo non-contrast CT images from contrast-enhanced images using GANs [37] has shown promise. However, both strategies introduce additional computational steps, highlighting the need to balance the overall processing efficiency carefully. Second, our detection method targets bleeding located from the chest to the lower extremities. Because trauma CT scan protocols differ across institutions, the scan range for delayed-phase contrast-enhanced CT is inconsistent. [PERSON] et al. [4] reported that a typical delayed-phase scan range encompasses the chest, abdomen, and pelvis. All cases in the multi-institutional dataset used in this study included these anatomical regions. As various CAD software already exists for detectingbleeding in head CT images, bleeding located in the head was excluded from the scope of this study. Third, our evaluation focused only on the stand-alone performance of the CAD software. An observer performance study is desirable to evaluate the overall performance of physicians assisted by CAD software.