Facilitating Timely Decision-Making in Healthcare: an Object Detection Approach for Automated Coronary Artery Stenosis Detection

Abstract

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.