This study aimed to provide a quantitative evaluation of the lung gas content in orthopedic surgery patients under different anesthesia using ultrasound images based on the artificial bee colony algorithm. The ultrasound image features based on an artificial bee colony algorithm were applied to analyze segmentation images to investigate the influence of different anesthesia methods on the lung air content of patients undergoing orthopedic surgery and the clinical features of such patients. They were also adopted for the anesthesia in orthopedic surgery to assist clinicians in the diagnosis of diseases. 160 orthopedic surgery patients who were hospitalized were treated with different anesthesia methods. The first group (traditional general anesthesia group) received general anesthesia and traditional ultrasound; the second group (ABC general anesthesia group) was used for ultrasound image analysis based on the artificial bee colony algorithm; the third group (traditional sclerosis group) was anesthetized with combined sclerosis block; ultrasound images of patients from the fourth group (ABC sclerosis group) were analyzed based on the artificial bee colony algorithm. Analysis was conducted at three time points. The LUS score of the traditional sclerosis group and ABC sclerosis group was hugely higher than the score of the traditional general anesthesia group and ABC general anesthesia group at T2 time, with statistical significance ( P < 0.005 ). At time point T3, the score of the traditional sclerosis group rose greatly compared with the general anesthesia group, and that of the ABC group was generally higher than that of the traditional ultrasound group ( P < 0.005 ). When the threshold value was 4, the fitness value of ABC algorithm was 2680.4461, and the fitness value of the control group was 1736.815. The difference between the two groups was 943.6311 ( P < 0.05 ). The operation time of ABC algorithm was 1.83, while that of the control group was 1.05, and the difference between the two groups was 0.78 ( P < 0.05 ). In conclusion, the feature analysis of ultrasonic images based on the artificial bee colony algorithm could effectively improve the accuracy of ultrasonic images and the accuracy of focus recognition. It can promote medical efficiency and accurately identify the lung air content of patients in future clinical case measurement and auxiliary treatment of fracture, which has great application potential in improving surgical anesthesia effect.

Objective. This study aimed to present an investigation of the clinical significance of magnetic resonance imaging (MRI) images obtained based on the backpropagation neural network (BPNN) artificial intelligence algorithm for hip arthroplasty under general anesthesia. Methods. In this study, a case-review method was used to collect 100 patients requiring total hip replacement. They were then randomly divided into an observation group and a control group. Based on the neural network algorithm, the images of the two groups of patients were analyzed to judge their accuracy. Then the sensitivity, specificity, and accuracy of MRI images based on neural algorithms were compared with those processed by radiologists. Results. It was found that MRI processed by BP neural network had good accuracy in the diagnosis of hip joint diseases compared with CT. Meanwhile, the images processed by BP neural network had good specificity and accuracy compared with the images processed by radiologists. Conclusion. Imaging images obtained by BPNN artificial intelligence algorithm were more accurate than CT images, which had more guiding value for surgeons in operation.

To explore whether preoperative processing can promote the recovery of gastrointestinal function after laparoscopic cholecystectomy (LC) surgery, in the study, an artificial intelligence-based algorithm was used to segment the CT images to assist doctors in decision making. The patients were divided into observation group (balanced anesthesia) and control group (general anesthesia) with SPSS. The observation group received balanced anesthesia half a day before the operation. The method of balanced anesthesia was to induce 0.2 mg/kg midazolam, 3 mg/kg propofol, 2 μg/kg remifentanil, 0.2 mg/kg vecuronium, 4∼5 mg/(kg·h) propofol, and 9∼11 μg/(kg·h) remifentanil continuous intravenous infusion to maintain anesthesia, and it was stopped once the patient defecated; the control group had general anesthesia in the afternoon after the operation, and it was stopped once the patient defecated. The time before the first exhaust and defecation after the surgery as well as the recovery time of bowel sound was recorded, and the degree of abdominal pain, abdominal distension, and gastrointestinal adverse reactions was evaluated at 22 hours, 46 hours, and 70 hours after the surgery. It was found that the accuracy of the artificial intelligence-based segmentation algorithm was 81%. The reconstruction accuracy of multidimensional liver could be observed at any angle, and the reconstruction accuracy was not lower than the resolution of original input CT. The calculation error was less than 9%, and the volume of whole liver, liver segment, preresection liver, and residual liver was less than 9%. The simulation accuracy of virtual liver surgery was not lower than the resolution of original input CT. The time before the first exhaust and defecation was shorter in the observation group versus the control group ( P  < 0.05). The recovery time of bowel sound in the observation group was shorter than that in the control group ( P  < 0.05). There was a significant difference in the scores of abdominal distension between the two groups at 22 h and 46 h after surgery ( P  < 0.05). It suggested that both the observation group and the control group could improve the symptoms of gastrointestinal adverse reactions after surgery. Nevertheless, balanced anesthesia can shorten the time before the first exhaust and defecation after surgery and promote the recovery of postoperative bowel sound. Furthermore, balanced anesthesia can alleviate abdominal distension, abdominal pain, and gastrointestinal adverse reactions, which should be promoted in clinic.