## 4 Discussion
This distinction is particularly important in complex anatomical configurations where traditional segmentation methods often struggle [34]. From a methodological standpoint, bounding box detection offers a simplified alternative to conventional voxel-based or centerline-based approaches. While segmentation methods can provide more detailed morphological information, they may inadvertently merge adjacent vascular structures in complex anatomical regions. Our approach aims to rapidly highlight relevant arterial segments, minimizing overlap errors and facilitating potential downstream analyses of stenosis or plaque burden. However, fully automated segmentation can be advantageous when a more precise quantification of vessel geometry is required, underscoring the complementary nature of these methods. This study has several limitations that should be acknowledged. First, while bounding box detection helps mitigate confusion with adjacent veins, it does not capture the vessel's full luminal geometry. For tasks requiring precise measurements of stenosis severity or plaque burden, additional segmentation or plaque quantification tools may be necessary. Second, our dataset, though comprising over 19,000 images, was derived from a relatively homogeneous patient cohort in a single institution. Future work would benefit from external validation using more heterogeneous data, especially in cases involving heavy calcifications, coronary stents, or bypass grafts. Unfortunately, expanding our study to include multi-institutional data or implementing extensive new experiments was beyond the scope of the current research due to regulatory and resource constraints. Similarly, the public release of patient data was not possible due to privacy regulations and institutional policies governing protected health information [35]. Third, interpretability remains an inherent challenge of deep learning; clinicians cannot always discern which features the network relies upon for its predictions. Recent advances in explainable AI techniques might offer potential solutions to this challenge in the future iterations of our model [36]. Fourth, we did not systematically compare YOLOX with other commonly used deep learning architectures (e.g., U-Net, Mask R-CNN, nnU-Net) in a head-to-head manner. Although we performed the initial segmentation trials using U-Net, frequent misclassifications of adjacent vascular structures prompted us to shift toward bounding box detection. A more comprehensive evaluation of multiple architectures would clarify the relative performance and feasibility of YOLOX-based detection, potentially revealing the advantages or limitations compared to other network designs. Finally, smaller secondary branches and rarer anatomical variants, which can be clinically significant, were not extensively evaluated in this study. Looking ahead, a promising extension of this work is to integrate bounding box detection with 3D visualization workflows, such as curved planar reformations (CPRs) or volume-rendered images, to further reduce operator dependence in generating clinically relevant views. The bounding boxes could serve as initial regions of interest for more advanced segmentation or computational fluid dynamics analyses. This integration could potentially address some of the current limitations in the quantitative assessment of coronary stenosis and plaque characterization [37]. As the volume of CCTA studies continues to grow, automating the detection and localization of coronary arteries has the potential to expedite clinical workflows and enhance diagnostic consistency. Further optimization and validation in diverse clinical settings will be essential to fully realize these benefits. Ultimately, bounding box-based object detection could become a key component in the standardization and automation of CCTA interpretation, contributing to better patient outcomes in the management of ischemic heart disease.