The application of artificial intelligence (AI) in perioperative anesthesia decision-making is becoming a research hotspot, particularly in anesthesia risk assessment, depth of anesthesia monitoring, and postoperative recovery management, where it demonstrates significant potential. AI technologies, especially machine learning and deep learning, have demonstrated exceptional capabilities in processing and analyzing high-dimensional complex data. By leveraging these technologies, it is possible to efficiently interpret vast amounts of intricate clinical data, thereby providing anesthesiologists with personalized and precise decision support. However, implementing AI technologies in clinical practice faces numerous challenges, mainly including data quality, algorithm interpretability, and technological compatibility. Furthermore, concerns surrounding data privacy and ethical considerations urgently need to be addressed to ensure that the application of AI technologies aligns with clinical ethics and legal standards. This article aims to provide a comprehensive overview of AI technologies, their applications in perioperative anesthesia decision-making, existing limitations, and future directions. The goal is to offer insights into clinical anesthesia practice and to promote the realization of personalized precision anesthesia.

Hypertrophic cardiomyopathy, a genetic hereditary disease, is highly regarded in clinical practice due to its unique pathophysiological changes, course characteristics, and hemodynamic features. With the continuous advance of treatment methods such as medications and surgeries, the prevention, treatment and prognosis of hypertrophic cardiomyopathy has gradually improved. However, inappropriate use of positive inotropic drugs may lead to serious consequences that are difficult to reverse. Therefore, how to smoothly navigate through the perioperative period and ensure clinical safety poses a great challenge for anesthesiologists. This paper discusses the perioperative management of patients with hypertrophic cardiomyopathy, with the hope of enhancing anesthesiologists' management capabilities for this type of disease.

The application of artificial intelligence (AI) in perioperative anesthesia decision-making is becoming a research hotspot, particularly in anesthesia risk assessment, depth of anesthesia monitoring, and postoperative recovery management, where it demonstrates significant potential. AI technologies, especially machine learning and deep learning, have demonstrated exceptional capabilities in processing and analyzing high-dimensional complex data. By leveraging these technologies, it is possible to efficiently interpret vast amounts of intricate clinical data, thereby providing anesthesiologists with personalized and precise decision support. However, implementing AI technologies in clinical practice faces numerous challenges, mainly including data quality, algorithm interpretability, and technological compatibility. Furthermore, concerns surrounding data privacy and ethical considerations urgently need to be addressed to ensure that the application of AI technologies aligns with clinical ethics and legal standards. This article aims to provide a comprehensive overview of AI technologies, their applications in perioperative anesthesia decision-making, existing limitations, and future directions. The goal is to offer insights into clinical anesthesia practice and to promote the realization of personalized precision anesthesia.

Objective To observe neuropsychological development status in children after surgical treatment of congenital heart diseases(CHDs)and analyze the risk factors. Methods 89 children who received outpatient review in Fuwai Hospital from September 2015 to March 2016 after surgical treatment of CHDs were recruited in this study and 90 normal children were recruited as the control group. The children with CHDs were divided into simple CHDs group(RACHS- 1 score≤2)and com-plex CHDs group(RACHS- 1 score≥3)according to RACHS- 1 classification. Neuropsychological development status was meas-ured according to pediatric-psychological mental test scale developed by Capital institute of pediatrics,Beijing and statistical a-nalysis was compared. Results The measurements of neuropsychological development showed the normal children behaved better than the children with CHDs(P < 0. 05). The simple CHDs group achieved better distribution of development quotient than complex CHDs group(P = 0. 032)and there was no difference between the normal control group and simple CHDs group (P = 0. 420). Multivariate regression analysis indicated that younger age at cardiac surgery,lower preoperative blood urea ni-trogen(BUN),higher preoperative creatinine(Cr)and prolonged duration of cardiopulmonary bypass(CPB)accounted for low-er scores in the test scale(P < 0. 05). Conclusion Distinct neuropsychological difficulties could be present especially in chil-dren with complex CHDs. Younger age at cardiac surgery,preoperative BUN,Cr and CPB duration were perioperative factors that were associated with long-time neuropsychological development.