# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 4 Discussion
While this choice was made to reflect clinical practice, it inherently introduced certain challenges. Specifically, some angiographic images, characterized by their complexity or intrinsic difficulty in identifying stenosis, were included. Consequently, our model's ability to achieve very high mean average precision (mAP) values was constrained in such instances. The presence of these challenging images underscores the practical and clinical variability encountered in real-world scenarios, necessitating further investigation into model robustnessunder such conditions. Secondly, the extraction of key frames from coronary angiography video streams, while a necessary step in our approach, came with inherent limitations. These limitations became particularly evident when dealing with branch vessels. Due to the complexities associated with the dynamic nature of coronary blood flow, obtaining a comprehensive set of key frames with branch vessels fully intact proved to be a challenging endeavor. Thirdly, although we conscientiously considered stenosis in branch vessels, many of these cases remained inadequately observed and labeled. This limitation subsequently impacted the model's capacity to effectively recognize and incorporate these branch vessel stenoses into its results. This highlights an area where future research efforts may be directed, with a focus on enhancing the model's ability to accurately detect stenosis in branch vessels within the context of coronary angiography. Fourthly, we employed an older version from the YOLO series, YOLOv4, instead of the latest version, YOLOv8. Our rationale for choosing YOLOv4 was its significant improvements over prior versions and its demonstrated stability and maturity in diverse environments. Although newer versions like YOLOv8 might offer enhanced features, they could be in experimental phases or lack thorough validation. We selected YOLOv4 for its proven reliability, considering it a cautious decision to enhance our study's dependability. The fifth point is that this study does not delve deeply into the optimization of learning parameters in the model training process, which is one of its limitations. The image training in this study is based on existing research and adopts a general approach to similar challenges. The training process, including measures to avoid overtraining, could have enhanced the model's accuracy and generalizability through appropriate parameter optimization. Future research is expected to conduct a more detailed analysis of these aspects, contributing to the improvement of model performance. These limitations underscore the necessity for ongoing research endeavors aimed at addressing these constraints and further refining our methodology. By acknowledging and actively addressing these limitations, we can progress towards a more robust and clinically applicable framework for coronary artery stenosis detection. Conceptualization, H.S. and Y.L. ; methodology, H.S. and Y.L. ; software, Y.L. ; validation, Y.L., T.Y. and Y.H. ; formal analysis, Y.L. ; investigation, Y.L. ; resources, H.S. ; data curation, Y.H. ; writing--original draft preparation, Y.L. ; writing--review and editing, T.Y. ; visualization, Y.L. ; supervision, H.S. ; project administration, H.S. ; funding acquisition, H.S. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by JST, the establishment of university fellowships towards the creation of science technology innovation, Grant Number JPMJFS2101. The study involving human participants was reviewed and approved by the Clinical Research Administration Center at Hokkaido University Hospital. Informed Consent StatementInformed consent was not obtained for this study, as it was a retrospective study.