Efficacy Evaluation of Ultrasound with Active Contour Model for Hemodialysis in Children with Renal Failure

This study was aimed to explore the efficacy of ultrasound with active contour model (ACM) for hemodialysis in children with renal failure. The pulse coupled neural network (PCNN) was used to extract the initial contour of the ultrasound images, and the cloud model-based ACM was used to accurately segment the images, whose effect was compared with the classic Snake model. 84 children with chronic renal failure who received hemodialysis treatment in hospital were selected as research objects. There were 42 cases in the control group who were diagnosed by conventional ultrasound and 42 cases in the observation group who were diagnosed by ultrasound with the algorithm. Then, 42 children who underwent healthy physical examination (health group) were selected for comparison of related analysis indicators. The error rates of different algorithms were compared to analyze the levels of inflammatory factors in different groups of patients after hemodialysis. The results showed that the error rate of classical Snake model was 18.87% and that of ACM algorithm model was 11.01%, and the error rate of ACM algorithm model was significantly lower (P < 0.05). After hemodialysis, the level of tumor necrosis factor (TNF)-α was 38.76 pg/mL in the observation group and 40.05 pg/mL in the control group, which was notably decreased in both groups, especially in the observation group (P < 0.05). After hemodialysis, transforming growth factor (TGF)-β1 was 7.76 ng/mL in the observation group and 7.60 ng/mL in the control group, which was significantly reduced in both groups. After treatment, UA and Scr in both groups were significantly reduced, and the reduction was more significant in the observation group (P < 0.05). HGB and RBC were significantly increased in both groups, and the increase was more significant in the observation group (P < 0.05). In summary, ACM algorithm had a good segmentation effect on the ultrasonic images of children with renal failure. This study provided guidance for clinicians to choose the algorithm for the application of ultrasonic imaging diagnosis.