Artificial Bee Colony Algorithm-Based Ultrasound Image Features in the Analysis of the Influence of Different Anesthesia Methods on Lung Air Volume in Orthopedic Surgery Patients

Abstract

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.