Abstract
In order to discuss the clinical characteristics of patients with scapular fracture, deep learning model was adopted in ultrasound images of patients to locate the anesthesia point of patients during scapular fracture surgery treated with the regional nerve block. 100 patients with scapular fracture who were hospitalized for emergency treatment in the hospital were recruited. Patients in the algorithm group used ultrasound-guided regional nerve block puncture, and patients in the control group used traditional body surface anatomy for anesthesia positioning. The ultrasound images of the scapula of the contrast group were used for the identification of the deep learning model and analysis of anesthesia acupuncture sites. The ultrasound images of the scapula anatomy of the patients in the contrast group were extracted, and the convolutional neural network model was employed for training and test. Moreover, the model performance was evaluated. It was found that the adoption of deep learning greatly improved the accuracy of the image. It took an average of 7.5 ± 2.07 minutes from the time the puncture needle touched the skin to the completion of the injection in the algorithm group (treated with artificial intelligence ultrasound positioning). The operation time of the control group (anatomical positioning) averaged 10.2 ± 2.62 min. Moreover, there was a significant difference between the two groups (p < 0.05). The method adopted in the contrast group had high positioning accuracy and good anesthesia effect, and the patients had reduced postoperative complications of patients (all P < 0.005). The deep learning model can effectively improve the accuracy of ultrasound images and measure and assist the treatment of future clinical cases of scapular fractures. While improving medical efficiency, it can also accurately identify patient fractures, which has great adoption potential in improving the effect of surgical anesthesia.
Highlights
Scapular fracture was first described by Desault in 1805, who studied the characteristics of scapular fracture
In the image before enhancement and optimization, the fracture at the bottom right of Figure 5 does not seem to be obvious. If it was not enhanced, it was easy to be misjudged as a nonfracture site and affect the doctor’s judgment, while the enhanced ultrasound image can clearly show the location of the fracture
A safe and effective guiding puncture method is urgently needed in clinic. e results showed that the fracture site was not obvious in the image before enhancement and optimization, and it was easy to be misjudged as a nonfracture site, affecting the judgment of doctors. e enhanced sonogram clearly showed the fracture location. e localization area of traditional ultrasound and artificial intelligence ultrasound overlapped greatly, and the area of artificial intelligence localization was 36% more than that of inner ultrasound. e algorithm group had fewer puncture needle channels, fewer adverse reactions, and broken operation time, indicating that the ultrasonic positioning effect of the algorithm was better than that of traditional puncture
Summary
Scapular fracture was first described by Desault in 1805, who studied the characteristics of scapular fracture. Ultrasound imaging can visualize the nerve that needs to be blocked, as well as the accompanying blood vessels and important tissues around the nerve. The research and application of in-depth learning in the field of anesthesia are relatively rare, and the risk of death of patients after general anesthesia can be predicted based on the data extracted during surgery. E purpose of this research was discussing the following three issues: first, the difference in image accuracy between deep learning ultrasound images and ordinary ultrasound images; second, the adoption of artificial intelligence ultrasound to optimize anesthesia puncture path; third, the effectiveness of ultrasonographic imaging guided scapular regional nerve block in the treatment of surgical pain of fracture. It was hoped to provide reference for other regional nerve block anesthesia operations
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