Relationship between Artificial Intelligence-Based General Anesthetics and Postoperative Cognitive Dysfunction.

Abstract

Postoperative cognitive dysfunction (POCD) refers to the complications of the central nervous system before and after surgery in patients without mental disorders. Many studies have shown that surgical anesthesia may cause POCD, especially in elderly patients. This article aims to study the relationship between artificial intelligence-based general anesthetics and postoperative cognitive dysfunction. This article first describes and classifies artificial intelligence, introduces its realization method, machine learning algorithms, and briefly introduces the basic principles of regression and classification methods in machine learning; then, the principles and techniques of general anesthetics are proposed. The pathogenesis of postoperative cognitive dysfunction (POCD) is explained in detail. Finally, the effect of anesthetics on postoperative cognitive dysfunction is obtained from both inhaled anesthetics and intravenous anesthetics. The impact on postoperative cognitive function is explained. The experimental results in this article show that there is no statistically significant difference in the two groups of patients' age, gender ratio, body mass index, education level, preoperative comorbidities, and other general indicators. Through the use of EEG bispectral index monitors to monitor the depth of anesthesia and postoperative cognitive dysfunction, first, there was no obvious relationship between the occurrence of postoperative cognitive dysfunction at 1, 5, 10, and 50 days and discharge time. The comprehensive monitoring group can reduce the clinical dose of preventive medication and cis-atracurium and shorten the patient's recovery time, extubation time, and recovery time. In addition, it can also reduce the increase of serum protein S100β in elderly patients and reduce the incidence of early postoperative cognitive dysfunction.