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.

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.

BACKGROUNDIn order to minimize the injury reaction during the surgery and reduce the morbidity rate, hence reducing the mortality rate of esophagectomy, minimally invasive esophagectomy (MIE) was introduced. The aim of this study was to compare the postoperative outcomes in patients with esophageal squamous cell carcinoma undergoing minimally invasive or open esophagectomy (OE).

METHODSThe medical records of 176 consecutive patients, who underwent minimally invasive esophagectomy (MIE) between January 2009 and August 2013 in Cancer Institute & Hospital, Chinese Academy of Medical Sciences, were retrospectively reviewed. In the same period, 142 patients who underwent OE, either Ivor Lewis or McKeown approach, were selected randomly as controls. The clinical variables of paired groups were compared, including age, sex, Charlson score, tumor location, duration of surgery, number of harvested lymph nodes, morbidity rate, the rate of leak, pulmonary morbidity rate, mortality rate, and hospital length of stay (LOS).

RESULTSThe number of harvested lymph nodes was not significantly different between MIE group and OE group (median 20 vs. 16, P = 0.740). However, patients who underwent MIE had longer operation time than the OE group (375 vs. 300 minutes, P < 0.001). Overall morbidity, pulmonary morbidity, the rate of leak, in-hospital death, and hospital LOS were not significantly different between MIE and OE groups. Morbidities including anastomotic leak and pulmonary morbidity, inhospital death, hospital LOS, and hospital expenses were not significantly different between MIE and OE groups as well.

CONCLUSIONSMIE and OE appear equivalent with regard to early oncological outcomes. There is a trend that hospital LOS and hospital expenses are reduced in the MIE group than the OE group.

Aged ; Carcinoma, Squamous Cell ; surgery ; Esophageal Neoplasms ; surgery ; Esophagectomy ; methods ; Female ; Humans ; Laparoscopy ; Length of Stay ; Male ; Middle Aged ; Minimally Invasive Surgical Procedures ; Thoracoscopy ; Treatment Outcome