The aim of this study was at exploring the clinical effect of CT images based on multiplanner reformation (MPR) combined with a preoperative psychological nursing intervention model in sinusitis patients undergoing general anesthesia. Sixty sinusitis patients who received MPR-based CT examination and general anesthesia were selected as the study subjects and randomly divided into the control group ( n = 30 ) and the experimental group ( n = 30 ). The control group used traditional preoperative education. The experimental group added the psychological nursing intervention based on traditional preoperative education. The blood pressure and heart rate before and after the operation, the self-rating anxiety scale (SAS) score before and after intervention, and satisfaction were comprehensively assessed. The results showed that CT based on MPR could observe the lesions and anatomical structures of the sinus wall and sinus in detail from multiple angles. The blood pressure (systolic blood pressure 135.12 ± 14.89 mmHg , diastolic blood pressure 87.05 ± 11.24 mmHg ), heart rate ( 78.42 ± 12.19 beats/min), SAS score ( 45.85 ± 4.97 points), and nursing satisfaction (78.9%) of the experimental group were significantly better than those of the control group ( 145.83 ± 15.62 mmHg , 94.21 ± 10.86 mmHg , 86.44 ± 13.65 beats/min, 56.44 ± 5.12 points, 56.4%), and the differences were statistically significant ( P < 0.05 ). In summary, the preoperative psychological care model has a positive role in reducing the tension and anxiety of patients before general anesthesia surgery and CT based on MPR is important for the clinical diagnosis and treatment of sinusitis. This study provides a theoretical reference for the clinical treatment of patients with sinusitis.
This research was aimed at analyzing the effect of humanized nursing intervention combined with computed tomography (CT) imaging in the surgical anesthesia of femur intertrochanteric fractures (FIF) in the elderly. An image reconstruction algorithm was proposed based on nonlocal mean (NLM) algorithm, which was named as ONLM, and its performance was analyzed. A total of 114 elderly patients with FIF were equally and randomly divided into a humanized nursing group (57 cases) and a routine nursing group (57 cases). They were performed with CT imaging scan based on the ONLM algorithm, and the clinical indicators of the two groups of patients were recorded. The root mean square error (RMSE) and mean absolute error (MAE) of the CT images constructed using the ONLM algorithm were significantly lower than those using NLM algorithm, edge filtering algorithm, and total variation model, while the peak signal-to-noise ratio (PSNR) was the opposite ( P < 0.05 ). The operation time, hospitalization days, intraoperative blood loss, postoperative drainage, and anesthesia preparation time of patients in the humanized nursing group were significantly lower than those in the routine nursing group. The number of patients with excellent Harris scores in the humanized nursing group was higher than that in the routine nursing group, and the number of patients with poor Harris scores was lower ( P < 0.05 ). The language pain score, facial pain score, and visual analog simulation (VAS) scores of patients in the humanized nursing group were significantly lower than those in the routine nursing group. The numbers of postoperative hip varus and fracture nonunion cases in the humanized nursing group were significantly more than those in the routine nursing group. In short, CT images constructed by the ONLM showed higher performance than those by the traditional algorithm. In addition, CT images constructed by ONLM combined with humanized nursing intervention could more effectively improve the cooperation of patients with surgical anesthesia, reduce surgical pain and fear of patients, improve the prognosis of patients, and lower the occurrence of adverse events.
To analyze the investigation of the application effects of different doses of dexmedetomidine (Dex) with combined spinal and epidural anesthesia nursing on analgesia after transurethral resection of prostate (TURP) by intelligent algorithm-based magnetic resonance imaging (MRI), MRI imaging segmentation model of mask regions with convolutional neural network (Mask R-CNN) features was proposed in the research. Besides, the segmentation effects of Mask R-CNN, U-net, and V-net algorithms were compared and analyzed. Meanwhile, a total of 184 patients receiving TURP were selected as the research objects, and they were divided into A, B, C, and D groups based on random number table method, each group including 46 cases. Patients in each group were offered different doses of Dex, and visual analogue scale (VAS) and Ramsay scores of different follow-up visit time, use of other analgesics, the incidence of postoperative cystospasm, and nursing satisfaction of patients in four groups were compared. The results demonstrated that Dice similarity coefficient (DSC) value, specificity, and positive predictive value of Mask R-CNN algorithm were 0.623 ± 0.084 , 98.61%, and 69.57%, respectively, all of which were higher than those of U-net and V-net algorithms. Pain VAS scores and the incidence of cystospasm at different time periods of groups B and C were both significantly lower than those of group D ( P < 0.05 ). Ramsay scores of groups B and C at 8 hours, 12 hours, 24 hours, and 48 hours after the operation were all remarkably higher than those in group D ( P < 0.05 ). Besides, nursing satisfaction of groups B and C was obviously superior to that in group D, and the difference demonstrated statistical meaning ( P < 0.05 ). The differences revealed that Dex showed excellent analgesic and sedative effects and could effectively reduce the incidence of complications after TURP, including cystospasm and nausea. In addition, it helped improve nursing satisfaction and patient prognosis.
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