The aim of this study was to investigate the therapeutic effect of minimally invasive aspiration on intracerebral hemorrhage (ICH) and the value of artificial intelligence algorithm combined with computed tomography (CT) image evaluation. Ninety-two patients with intracerebral hemorrhage were divided into experimental group (46 cases, minimally invasive aspiration therapy) and control group (46 cases, traditional craniotomy therapy) according to different treatment methods, and CT image scanning was performed. In addition, a CT image segmentation model of intracerebral hemorrhage based on improved fuzzy C-means clustering algorithm (n-FCM) was proposed to process the CT images of the patients. The results showed that the Dice coefficient of n-FCM algorithm after the addition of salt and pepper noise was 0.89, which was higher than that of traditional algorithm; the average operation time of experimental group was 58.93 ± 5.33 min, which was significantly lower than that of control group (90.21 ± 16.24 min) (P < 0.05); the overall response rate of experimental group was 93.48%, which was significantly higher than that of control group (76.09%) (P < 0.05); one month after operation, the National Institutes of Health Stroke Scale (NIHSS) score of experimental group was 3.89 ± 1.95 points, and the Scandinavian Stroke Scale (SSS) score was 10.67 ± 1.76 points, which was significantly lower than that of control group (P < 0.05); the incidence rate of complications in experimental group was significantly lower than that of control group (P < 0.05). It showed that the n-FCM algorithm was superior to the traditional algorithm in CT image processing, with the advantages of good denoising effect and less running time. Minimally invasive aspiration treatment had the advantages of operation time, convenient operation, and less damage to patients, which was beneficial to postoperative recovery and prognosis of patients.
Copyright © 2022 Junfeng Sun et al.