Application Analysis of Artificial Intelligence Target Controlled Infusion in Clinical Anesthesia Operation

Li Yuan, Guo-li Li, Zhitao Teng, Jin-liang Teng
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Abstract

Medical data are too scattered, complex and ethically difficult. Its development in the field of surgery is still in the initial stage of exploration. But with the development of machine deep learning, it is necessary for surgeons and data experts to strengthen interdisciplinary cross-disciplinary cooperation in order to promote the progress of AI in the field of surgical operation, and finally realize AI driven automatic robot for surgical operation. This process is long and hard, which requires a long time of effort from surgeons and data experts, and steadily promote the development of artificial intelligence, so as to achieve achievements. Many clinical operations require anesthesia, local anesthesia or general anesthesia. Artificial anesthesia can not control the dose and concentration of drugs accurately, which leads to the prolonged recovery time of patients and affects physiological function. In addition, the related research confirmed that target controlled intravenous anesthesia can effectively maintain the plasma drug concentration during operation, achieve the ideal anesthesia depth and reduce the stress response of operation. This paper discusses the application of artificial intelligence technology and target controlled infusion in clinical anesthesia. With the aid of the big data learning function of artificial intelligence, the paper analyzes the combination of body indexes of each patient and data of database, and summarizes the anesthesia infusion scheme for each patient. 84 patients who underwent laparoscopic appendicitis in a third class hospital from June 2018 to June 2019 were randomly divided into two groups, 42 patients in each group. The ratio of the number of men and women in the control group was 21:21, the age was between 18 and 55 years old; the ratio of the number of men and women in the control group was 21:21, and the age was between 18 and 55 years. There was no significant difference between the two groups (P > O.05), which was in line with the requirements of comparison.
人工智能靶控输液在临床麻醉手术中的应用分析
医疗数据过于分散、复杂,在伦理上存在困难。其在外科领域的发展尚处于探索的初级阶段。但随着机器深度学习的发展,需要外科医生和数据专家加强跨学科的交叉合作,才能推动人工智能在外科手术领域的进步,最终实现人工智能驱动的外科手术自动机器人。这个过程漫长而艰辛,需要外科医生和数据专家的长期努力,稳步推进人工智能的发展,从而取得成果。许多临床手术需要麻醉,局部麻醉或全身麻醉。人工麻醉不能准确控制药物的剂量和浓度,导致患者恢复时间延长,影响生理功能。此外,相关研究证实,靶控静脉麻醉可有效维持术中血浆药物浓度,达到理想麻醉深度,降低手术应激反应。本文探讨了人工智能技术和靶控输注在临床麻醉中的应用。借助人工智能的大数据学习功能,结合每位患者的身体指标与数据库数据进行分析,总结出每位患者的麻醉输注方案。选取2018年6月至2019年6月在某三甲医院行腹腔镜阑尾炎手术的84例患者,随机分为两组,每组42例。对照组男女人数之比为21:21,年龄在18 ~ 55岁之间;对照组男女人数之比为21:21,年龄在18 ~ 55岁之间。两组间差异无统计学意义(P > 0.05),符合比较要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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