Nursing Intervention of Children’s Lower Limb Chronic Wound Healing Under Artificial Intelligence

Abstract

This work aimed to compare the effect of family-centered nursing and routine nursing on the treatment of children with chronic lower limb wounds. A total of 112 children with chronic wounds of the lower limbs were divided into a test group (56 cases) and a control group (56 cases). The children in the test group were given a family-centered nursing intervention, while those in the control group were given a routine nursing intervention. Additionally, an image segmentation algorithm based on a deep convolutional neural network was proposed to segment chronic wound images. The results showed that there were no significant differences in sex, injury site (left or right limb), age, height, weight, and cause of disease (scald, blow, fall) between the test group and the control group (P > 0.05). The wound pain score of the test group was 3.38 ± 0.75 and that of the control group was 6.24 ± 1.48. Compared with the control group, the wound pain score of the test group was lower (P < 0.05). The total effective rate of the test group (92.86%) was significantly higher than that of the control group (78.57%) (P < 0.05). The accuracy of the DCNN model in both the training and test datasets was significantly higher than that of the FCM model (P < 0.05). The results showed that, compared with routine nursing, family-centered nursing intervention had a more significant effect on children with chronic wounds of the lower extremities and had clinical promotion value.

Keywords

• Deep convolutional neural networks
• family-centered nursing
• chronic wounds in children’s lower limbs
• image segmentation
• wound pain