Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 07 24 1015 1015 EN Hadis Keshavarz Cardiovascular Diseases Research Center, Department of Cardiology, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran Hossein Sadr Department of Health Informatics and Intelligent system, Guilan Road Trauma Research Center, Trauma institute, Guilan University of Medical Sciences, Rasht, Iran Mojdeh Nazari Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Arsalan Salari Department of Cardiology, Cardiovascular Diseases Research Center, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran 2024 05 21 2025 04 09 Purpose: In the era of digital medicine, medical imaging serves as a widespread technique for early disease detection, with a substantial volume of images being generated and stored daily in electronic patient records. X-ray angiography imaging is a standard and one of the most common methods for rapidly diagnosing coronary artery diseases. Deep neural networks, leveraging abundant data, advanced algorithms, and powerful computational capabilities, prove highly effective in the analysis and interpretation of images. In this context, Object detection methods have become a promising approach. Materials and Methods: Deep learning-based object detection models, namely RetinaNet and EfficientDet D3 were utilized to precisely identify the location of coronary artery stenosis from X-ray angiography images. To this aim, data from about a hundred patients with confirmed one-vessel coronary artery disease who underwent coronary angiography at the Research Institute for Complex Problems of Cardiovascular Diseases in Kemerovo, Russia was utilized. Results: Based on the results of experiments, almost both models were able to accurately detect the location of stenosis. Accordingly, RetinaNet and EfficientDet D3 detected the location of false stenotic segments with a probability of more than 93% in the coronary artery. Conclusion: It can be stated that our proposed model enables automatic and real-time detection of stenosis locations, assisting in the crucial and sensitive decision-making process for healthcare professionals. https://fbt.tums.ac.ir/index.php/fbt/article/view/1015 https://fbt.tums.ac.ir/index.php/fbt/article/download/1015/514