Effect of Multichannel Convolutional Neural Network-Based Model on the Repair and Aesthetic Effect of Eye Plastic Surgery Patients

OBJECTIVE: This study is aimed at exploring the impact of eye model based on multichannel convolutional neural network (CNN) on eye plastic surgery and aesthetic effect, thus formulating methods to improve the effect of eye plastic surgery. METHODS: A total of 64 patients who underwent pouch plastic surgery from January 2020 to March 2021 were selected as the research objects and were divided into observation group and control group by random number table method. The subjects in the observation group were evaluated by multichannel CNN-based eye model and doctors' experience, while those in the control group were evaluated by doctors' experience only, with 32 cases in both groups. Blepharoplasty, lower eyelid skin wrinkles, skin luster, and aesthetic scores were compared between the two groups. RESULTS: The similarity between the multichannel CNN model detected shape and the actual eye shape (98.78%) was considerably higher than that of the CNN model detected shape (78.65%) (P < 0.05). After treatment, the indexes of pouch degree, lower eyelid skin wrinkle, eyelid lacrimal sulcus, skin gloss, and aesthetic score in the observation group were better than those in the control group (P < 0.05). The incidence of complications in the observation group (13%) was considerably lower than that in the control group (28%) (P < 0.05). CONCLUSION: The eye model based on the multichannel CNN model was helpful to improve the surgical repair and aesthetic effect of patients and can improve the occurrence of postoperative complications.