Care of Maintenance Hemodialysis Patients through Intelligent Algorithm-Based Low-Dose Digital Subtraction Angiography

Abstract

Objective. The study aimed to explore the application value of artificial intelligence (AI)-based low-dose digital subtraction angiography (DSA) in the care of maintenance hemodialysis (MHD) patients. Methods. The characteristics of DSA imaging were analyzed, and the refinement efficiency of the AI algorithm was discussed, expected to assist clinicians in the care and treatment of patients. 100 MHD patients who were in the hospital were selected as the research subjects. They were randomly divided into the conventional DSA group (conventional group) and the AI algorithm-based DSA group (AI-based DSA group). The conventional group used conventional DSA images to guide the care of HM patients, and the AI-based DSA group used the AI algorithm to optimize DSA images. Results. It was found that the AI-based DSA group was better than the conventional DSA group in terms of image sharpness and shaded areas, and the image mean square error (MSE) loss value was smaller ( $P<0.05$ ). The patients were followed up for 3 months. In the AI-based DAS group, the blood flow of the drainage vein (DV), the blood flow of the proximal vein (PA), and the blood flow of the brachial artery (BA) were greater than those of the conventional group ( $P<0.05$ ). During the 3-month follow-up period, in the conventional group, thrombosis occurred in 4 patients, low-flow AVF occurred in 5 patients, high-flow AVF occurred in 3 patients, and heart failure occurred in 5 patients. In the AI-based DSA group, thrombosis occurred in 2 patients, low-flow AVF occurred in 2 cases, high-flow AVF occurred in 1 case, and heart failure occurred in 3 cases. There were no other cardiac complications in both groups. Conclusion. DSA images optimized by the AI algorithm are suitable for clinical diagnosis and have practical application value.