Automated Stenosis Detection in Coronarography Image Data

Coronary Artery Disease (CAD) represents a life-threatening condition resulting from the constriction or obstruction of coronary arteries. Timely detection plays a critical role in effective treatment. This paper introduces an innovative machine-learning approach that utilizes the ResNet architecture to automate stenosis identification in coronarography. The proposed method aims to enhance the efficiency and reliability of CAD diagnosis, providing valuable support to medical practitioners. Results emphasize the significance of high-quality training datasets in achieving precise stenosis detection. Discussion is made regarding study limitations, including dataset artifacts, and avenues for future research are proposed. This approach establishes a foundation for advancements in coronary vessel stenosis detection, with potential for classification and additional feature extraction.Clinical relevance: The proposed method aims at the automation of CAD - a world-leading cause of death - diagnosis and personalized treatment suggestions. AI-supported detection and characterization facilitates cardiologist work required for manual data analysis.