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

Jianhua Hong,M Pallikonda Rajasekaran

doi:10.1155/2021/8746962

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.

Highlights

Chronic kidney disease (CKD) is a global concern [1]
100 maintenance hemodialysis (MHD) patients who were in the hospital were selected as the research subjects. ey were randomly divided into the conventional Digital subtraction angiography (DSA) group and the artificial intelligence (AI) algorithm-based DSA group (AI-based DSA group). e 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
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

Summary

Introduction

Chronic kidney disease (CKD) is a global concern [1]. Statistics reveal that the incidence of CKD in China is as high as 10.8%, and CKD is often diagnosed in the late stage of kidney disease. Digital subtraction angiography (DSA) is a medical imaging system based on X-rays used in clinical angiography [4]. DSA is necessary for the treatment of vascular diseases. DSA images have high resolution and a wide range of gray levels, which can accurately present blood vessel. DSA has problems such as the low quality of blood vessel subtraction images and high radiation dose, and the there is a lot of noise in the final DSA image. Longterm exposure to X-ray will cause irreversible damage to human cells and DNA. In severe cases, it sometimes causes leukemia or cancer. E DSA artifact removal network has an end-to-end structure, and the parameters of the training process are set as follows.

Objectives

Methods

Results

Conclusion