Evaluation of Effect of Acupoint Sticking Therapy on Idiopathic Edema by Ultrasound Imaging Based on Deep Learning Algorithm

This study was aimed to evaluate the therapeutic effect of acupoint application therapy on specific edema by using denoising convolutional neural network (DnCNN) ultrasound images. 144 patients with idiopathic edema were selected as the research objects, and they were randomly divided into the conventional treatment group and the acupoint application group. Traditional Chinese medicine (TCM) symptom score, quality-of-life score, and adverse reactions were compared between the two groups before and after treatment. Ultrasound images based on the deep learning algorithm were used to evaluate the efficacy. The results showed that compared with conventional ultrasound, ultrasound based on DnCNN was more accurate in the diagnosis of idiopathic edema. The total effective rate of edema treatment in the acupoint application group was higher than that in the conventional treatment group (45% vs 93%, P < 0.01). After treatment, the scores of TCM symptoms in the acupoint application group were lower than those in the conventional treatment group, and the quality-of-life score in the acupoint application group was higher than that in the conventional treatment group (P < 0.05). There was no significant difference in the incidence of adverse reactions between the two groups (P > 0.05). In summary, ultrasound-assisted examination based on the algorithm is accurate in the diagnosis of idiopathic edema. Acupoint application therapy has a significant effect on idiopathic edema and does not increase the incidence of adverse reactions, which has a guiding role in clinical treatment.