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22 February 2023: Review Articles

Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security

Bijia Song 1ABCDE , Ming Zhou 2AB , Junchao Zhu 1ABCDEFG*

DOI: 10.12659/MSM.938835

Med Sci Monit 2023; 29:e938835

Table 1 Prospect of AI in the development of anesthesia.

AI in preoperative medicineAI in the deep monitoring and regulation of anesthesiaIntelligent automatic drug administration systemAI in the operation of basic anesthesia skillsAI in postoperative analgesiaAI in teaching and training in Anesthesia
Predict body mass index and thyroid distance and describe computerized facial analysis and photographs [–]19 Mirsadeghi et al [] used the ML algorithm to analyze the EEG power, total power, spindle score, and entropy in different bands to reflect the EEG characteristics of the awake-anesthesia state. The results showed that the ML algorithm could achieve more accurate effects than BIS (88.4% vs 84.2%)30 Absalom uses the proportional-integral-derivative (PID) control algorithm for anesthesia induction with a uniform effect chamber concentration and then for anesthesia maintenance with its designed closed-loop target control infusion system. This prevents drastic oscillations in the depth of anesthesia and reduces the control error []36 Lederman et al. [] began to use the Gaussian mixed model framework to learn and train the anatomical characteristics such as esophageal, upper tracheal segment, and tracheal bulge; the system determined that the correct rate of endotracheal intubation could reach 95%43 An ML model reported by Mišic et al. was proposed that could be used to predict a patient’s risk of readmission at 30 days postoperatively, with the area under the curve values ranging from 0.85 to 0.87. The model’s performance was confirmed using data from the first 36 hours postoperatively, with the advantage that the patient was still in the hospital []50 Zhou et al [] conducted a study of 60 practice nurses during the pandemic. This study aims to analyze the effectiveness of the micro-video method for distance teaching of Massive Open Online Course (MOOC). This is compared with the traditional teaching model. It was observed that the overall satisfaction of teachers and students in the MOOC group was higher63
Kendale et al used logistic regression, random forest, support vector machine, Naive Bayes, k-nearest neighbors, linear discriminant analysis, neural network, and gradient boosting for modeling. Which could successfully predict the occurrence of hypotension after general anesthesia []24 Park et al [] developed and optimized the DoA monitoring system based on real-time EEG and a deep neural network (DNN) algorithm, enabling real-time and accurate prediction within 20 ms. The performance is significantly better than that of BIS32 Schamberg et al [] developed a deep learning neural network based on pharmacodynamics, which is trained in a simulated environment using a “cross-entropy” method, aiming to control the depth of anesthesia. The advantage of the model lies in determining the appropriate dose for each patient reaching different depths of anesthesia and is more robust than the PID model in terms of individual differences in pharmacogenetics and efficacy37 The neural network model constructed by Hetherington et al can automatically identify the vertebral body, vertebral space, and other anatomical positioning and assist anesthesiologists in epidural puncture and catheterization, with accuracy as high as 95% []45 Ai-PCA is a new analgesic management system developed in recent years, when patients feel mild pain, the pain can be relieved by pressing the automatic control button. Ai-PCA can indicate “insufficient analgesia” or “poor analgesia” by analyzing the press frequency and reminding the APS medical staff to adjust the infusion dose of analgesic drugs in time to avoid the occurrence of moderate and severe pain. Applying the Ai-PCA system effectively reduced the incidence of postoperative pain and related adverse reactions, and patient satisfaction was significantly improved [,]58 Virtual patients are used to develop clinical examination skills, procedural learning, and communication skills while working with actual patients62. Meanwhile, the role of simulators has grown in importance during the health crisis of the pandemic. This is used as a complementary learning tool to impart clinical skills []64
Hatib et al [] determined the compensatory ability of the circulatory system and created a hypotension prediction model according to the characteristics of the invasive arterial pressure waves (arterial pressure wave time, wave amplitude, area under the curve, slope features, Flotrac algorithm features, CO-Trek features, baroreflex features, etc.). The advantage of the model is that the probability of hypotension occurrence can be predicted 15 min before its onset, with a sensitivity of 88% and a specificity of 87%25 Gu et al [] evaluated DoA based on multiple EEG frequency domains, and entropy features combined with an ANN, with high classification accuracy for awake, shallow, and moderate anesthesia33 The emergence of AI can help anesthesiologists analyze complex ultrasound data. The constructed machine learning algorithm can automatically measure parameters such as the heart’s ejection fraction and quickly evaluate heart function []46
Thottakkara et al [] used logistic regression and generalized additive model, Naive Bayes, support vector machine, and other ML methods to analyze 50 318 high-dimensional clinical data information from the University of Florida Health database to establish postoperative sepsis and acute kidney injury prediction models. The generalized additive model and support vector machine algorithm significantly improved the prediction performance28 Ramaswamy et al [,] extracted EEG spectrum features using clinical trial datasets, logistic regression, support vector machine, and random forest model training. The results showed that the EEG pattern trained by the RF model was similar to non-NREM sleep phase 3 and accurately predicted its sedation depth34 Gil applied the machine learning algorithm to the segmentation of ultrasound neural images. The feature extraction stage adopted a specific nonlinear wavelet transformation and adopted Gaussian processing to realize the automatic identification of the neural structure []Pesteie et al [] used a CNN to identify the prelaminar base automatically. In addition, Hetherington et al [] used a CNN to automatically identify the sacrum and L1–L5 vertebrae and vertebral spaces in real-time from ultrasound images with up to 95% accuracy48

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750