<p>Aortic valve calcium scoring is an essential tool for diagnosing, treating, monitoring, and assessing the risk of aortic stenosis. The current gold standard for determining the aortic valve calcium score is computed tomography (CT). However, CT is costly and exposes patients to ionizing radiation, making it less ideal for frequent monitoring. Echocardiography, a safer and more affordable alternative that avoids radiation, is more widely accessible, but its variability between and within experts leads to subjective interpretations. Given these limitations, there is a clear need for an automated, objective method to measure the aortic valve calcium score from echocardiography, which could reduce costs and improve patient safety. In this paper, we first employ the YOLOv5 method to detect the region of interest in the aorta within echocardiography images. Building on this, we propose a novel approach that combines UNet and diffusion model architectures to segment calcified areas within the identified region, forming the foundation for automated aortic valve calcium scoring. This architecture leverages UNet’s localization capabilities and the diffusion model’s strengths in capturing fine-grained structures, enhancing both segmentation accuracy and consistency. The proposed method achieves 85.08% precision, 80.01% recall, and 71.13% Dice score on a novel dataset comprising 160 echocardiography images from 86 distinct patients. This system enables cardiologists to focus more on critical aspects of diagnosis by providing a faster, more objective, and cost-effective method for aortic valve calcium scoring and eliminating the risk of radiation exposure.</p>