Calculation of Aortic Arch Calcification Degree in Hemodialysis Patients Using Deep Learning

Abstract

Abdominal Aortic Calcification (AAC) is a common form of vascular calcification closely associated with atherosclerosis and serves as an important marker for measuring increased risk of cardiovascular, cerebrovascular, and peripheral vascular diseases. Particularly in patients with Chronic Kidney Disease (CKD) and those undergoing dialysis, the risk of AAC significantly increases due to a combination of traditional and non-traditional risk factors. Therefore, developing a rapid and accurate method to assess the extent of AAC is crucial for preventing the progression of vascular calcification and the associated risk of cardiovascular diseases. Dialysis patients are required to undergo an abdominal X-ray annually, and the degree of calcification of the abdominal aorta is assessed manually through these X-ray images. However, these methods have limitations in identifying subtle calcifications in the abdominal aorta and the assessment process is time-consuming and depends on the experience and subjective judgment of physicians. To overcome these limitations, we propose a new method that incorporates deep learning technology to improve the accuracy of assessing the extent of AAC. Our method utilizes CNN models and attention modules to enhance the model's ability to recognize features of abdominal aortic calcification.