Removal of Autogenous Fat Filling in Double Eyelid Operation by Artificial Intelligence (AI) Algorithm-Based Computerized Tomography (CT) Image Features

Abstract

The effect of autogenous fat filling was evaluated by computerized tomography (CT) image features based on the symmetric extended convolutional residual network image denoising algorithm in this research, and the pathological examination was conducted for the lesions of patients with complications. The examination provided more valid research basis for the clinical application of autogenous fat filling. 60 patients who received double eyelid operation were selected as the research objects, and the patients were randomly divided into the control group and the experimental group, where each group included 30 cases. The conventional double eyelid operation was adopted in treating the cases in the control group, while autogenous fat filling was adopted in the treatment of the cases in the experimental group. All patients in two groups were examined by CT images based on the symmetric extended convolutional residual network image denoising algorithm, and the therapeutic effects on patients in two groups and the evaluation of complications were compared. Next, the lesions of the complications of patients in the experimental group were examined pathologically. Besides, the efficacy and security of the pathological examination were assessed. The result showed that the values of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) (σ (noise level) = 60 : 24.78 dB, 0.7022) in different noise images of the artificial intelligence algorithm adopted in the research were obviously higher than those obtained by the convolutional neural network (CNN) and deep convolutional neural network (DCNN) algorithms (P < 0.05). The therapeutic efficacy of patients in the experimental group was higher than that in the control group (68.67% vs 60%). In addition, eyelid swelling scoring, double eyelid line width, and upper eyelid muscle strength of patients in the experimental group were all better than those of patients in the control group (P < 0.05). Besides, the incidence of complications (10%) of patients in the experimental group was significantly lower than that (30%) of patients in the control group. The pathological results of patients in the experimental group demonstrated that the lesion tissues might denature in future. As a result, the CT image processing of the algorithm adopted in this research could denoise effectively, and PSNR and SSIM values were high. In terms of the treatment by double eyelid operation, autogenous fat filling was effective, noninvasive, simple and resulted in low incidence of complications with a certain degree of security.